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Ghost on the Shell: An Expressive Representation of General 3D Shapes | The creation of photorealistic virtual worlds requires the accurate modeling
of 3D surface geometry for a wide range of objects. For this, meshes are
appealing since they 1) enable fast physics-based rendering with realistic
material and lighting, 2) support physical simulation, and 3) are
memory-efficient for modern graphics pipelines. Recent work on reconstructing
and statistically modeling 3D shape, however, has critiqued meshes as being
topologically inflexible. To capture a wide range of object shapes, any 3D
representation must be able to model solid, watertight, shapes as well as thin,
open, surfaces. Recent work has focused on the former, and methods for
reconstructing open surfaces do not support fast reconstruction with material
and lighting or unconditional generative modelling. Inspired by the observation
that open surfaces can be seen as islands floating on watertight surfaces, we
parameterize open surfaces by defining a manifold signed distance field on
watertight templates. With this parameterization, we further develop a
grid-based and differentiable representation that parameterizes both watertight
and non-watertight meshes of arbitrary topology. Our new representation, called
Ghost-on-the-Shell (G-Shell), enables two important applications:
differentiable rasterization-based reconstruction from multiview images and
generative modelling of non-watertight meshes. We empirically demonstrate that
G-Shell achieves state-of-the-art performance on non-watertight mesh
reconstruction and generation tasks, while also performing effectively for
watertight meshes. | [
"Zhen Liu",
"Yao Feng",
"Yuliang Xiu",
"Weiyang Liu",
"Liam Paull",
"Michael J. Black",
"Bernhard Schölkopf"
] | 2023-10-23 17:59:52 | http://arxiv.org/abs/2310.15168v1 | http://arxiv.org/pdf/2310.15168v1 | 2310.15168v1 |
Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition | Federated Learning (FL) is a promising research paradigm that enables the
collaborative training of machine learning models among various parties without
the need for sensitive information exchange. Nonetheless, retaining data in
individual clients introduces fundamental challenges to achieving performance
on par with centrally trained models. Our study provides an extensive review of
federated learning applied to visual recognition. It underscores the critical
role of thoughtful architectural design choices in achieving optimal
performance, a factor often neglected in the FL literature. Many existing FL
solutions are tested on shallow or simple networks, which may not accurately
reflect real-world applications. This practice restricts the transferability of
research findings to large-scale visual recognition models. Through an in-depth
analysis of diverse cutting-edge architectures such as convolutional neural
networks, transformers, and MLP-mixers, we experimentally demonstrate that
architectural choices can substantially enhance FL systems' performance,
particularly when handling heterogeneous data. We study 19 visual recognition
models from five different architectural families on four challenging FL
datasets. We also re-investigate the inferior performance of convolution-based
architectures in the FL setting and analyze the influence of normalization
layers on the FL performance. Our findings emphasize the importance of
architectural design for computer vision tasks in practical scenarios,
effectively narrowing the performance gap between federated and centralized
learning. Our source code is available at
https://github.com/sarapieri/fed_het.git. | [
"Sara Pieri",
"Jose Renato Restom",
"Samuel Horvath",
"Hisham Cholakkal"
] | 2023-10-23 17:59:16 | http://arxiv.org/abs/2310.15165v1 | http://arxiv.org/pdf/2310.15165v1 | 2310.15165v1 |
Linear Representations of Sentiment in Large Language Models | Sentiment is a pervasive feature in natural language text, yet it is an open
question how sentiment is represented within Large Language Models (LLMs). In
this study, we reveal that across a range of models, sentiment is represented
linearly: a single direction in activation space mostly captures the feature
across a range of tasks with one extreme for positive and the other for
negative. Through causal interventions, we isolate this direction and show it
is causally relevant in both toy tasks and real world datasets such as Stanford
Sentiment Treebank. Through this case study we model a thorough investigation
of what a single direction means on a broad data distribution.
We further uncover the mechanisms that involve this direction, highlighting
the roles of a small subset of attention heads and neurons. Finally, we
discover a phenomenon which we term the summarization motif: sentiment is not
solely represented on emotionally charged words, but is additionally summarized
at intermediate positions without inherent sentiment, such as punctuation and
names. We show that in Stanford Sentiment Treebank zero-shot classification,
76% of above-chance classification accuracy is lost when ablating the sentiment
direction, nearly half of which (36%) is due to ablating the summarized
sentiment direction exclusively at comma positions. | [
"Curt Tigges",
"Oskar John Hollinsworth",
"Atticus Geiger",
"Neel Nanda"
] | 2023-10-23 17:55:31 | http://arxiv.org/abs/2310.15154v1 | http://arxiv.org/pdf/2310.15154v1 | 2310.15154v1 |
Verb Conjugation in Transformers Is Determined by Linear Encodings of Subject Number | Deep architectures such as Transformers are sometimes criticized for having
uninterpretable "black-box" representations. We use causal intervention
analysis to show that, in fact, some linguistic features are represented in a
linear, interpretable format. Specifically, we show that BERT's ability to
conjugate verbs relies on a linear encoding of subject number that can be
manipulated with predictable effects on conjugation accuracy. This encoding is
found in the subject position at the first layer and the verb position at the
last layer, but distributed across positions at middle layers, particularly
when there are multiple cues to subject number. | [
"Sophie Hao",
"Tal Linzen"
] | 2023-10-23 17:53:47 | http://arxiv.org/abs/2310.15151v1 | http://arxiv.org/pdf/2310.15151v1 | 2310.15151v1 |
Online Detection of AI-Generated Images | With advancements in AI-generated images coming on a continuous basis, it is
increasingly difficult to distinguish traditionally-sourced images (e.g.,
photos, artwork) from AI-generated ones. Previous detection methods study the
generalization from a single generator to another in isolation. However, in
reality, new generators are released on a streaming basis. We study
generalization in this setting, training on N models and testing on the next
(N+k), following the historical release dates of well-known generation methods.
Furthermore, images increasingly consist of both real and generated components,
for example through image inpainting. Thus, we extend this approach to pixel
prediction, demonstrating strong performance using automatically-generated
inpainted data. In addition, for settings where commercial models are not
publicly available for automatic data generation, we evaluate if pixel
detectors can be trained solely on whole synthetic images. | [
"David C. Epstein",
"Ishan Jain",
"Oliver Wang",
"Richard Zhang"
] | 2023-10-23 17:53:14 | http://arxiv.org/abs/2310.15150v1 | http://arxiv.org/pdf/2310.15150v1 | 2310.15150v1 |
Unlocking the Transferability of Tokens in Deep Models for Tabular Data | Fine-tuning a pre-trained deep neural network has become a successful
paradigm in various machine learning tasks. However, such a paradigm becomes
particularly challenging with tabular data when there are discrepancies between
the feature sets of pre-trained models and the target tasks. In this paper, we
propose TabToken, a method aims at enhancing the quality of feature tokens
(i.e., embeddings of tabular features). TabToken allows for the utilization of
pre-trained models when the upstream and downstream tasks share overlapping
features, facilitating model fine-tuning even with limited training examples.
Specifically, we introduce a contrastive objective that regularizes the tokens,
capturing the semantics within and across features. During the pre-training
stage, the tokens are learned jointly with top-layer deep models such as
transformer. In the downstream task, tokens of the shared features are kept
fixed while TabToken efficiently fine-tunes the remaining parts of the model.
TabToken not only enables knowledge transfer from a pre-trained model to tasks
with heterogeneous features, but also enhances the discriminative ability of
deep tabular models in standard classification and regression tasks. | [
"Qi-Le Zhou",
"Han-Jia Ye",
"Le-Ye Wang",
"De-Chuan Zhan"
] | 2023-10-23 17:53:09 | http://arxiv.org/abs/2310.15149v1 | http://arxiv.org/pdf/2310.15149v1 | 2310.15149v1 |
Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning | The pre-train and fine-tune paradigm in machine learning has had dramatic
success in a wide range of domains because the use of existing data or
pre-trained models on the internet enables quick and easy learning of new
tasks. We aim to enable this paradigm in robotic reinforcement learning,
allowing a robot to learn a new task with little human effort by leveraging
data and models from the Internet. However, reinforcement learning often
requires significant human effort in the form of manual reward specification or
environment resets, even if the policy is pre-trained. We introduce RoboFuME, a
reset-free fine-tuning system that pre-trains a multi-task manipulation policy
from diverse datasets of prior experiences and self-improves online to learn a
target task with minimal human intervention. Our insights are to utilize
calibrated offline reinforcement learning techniques to ensure efficient online
fine-tuning of a pre-trained policy in the presence of distribution shifts and
leverage pre-trained vision language models (VLMs) to build a robust reward
classifier for autonomously providing reward signals during the online
fine-tuning process. In a diverse set of five real robot manipulation tasks, we
show that our method can incorporate data from an existing robot dataset
collected at a different institution and improve on a target task within as
little as 3 hours of autonomous real-world experience. We also demonstrate in
simulation experiments that our method outperforms prior works that use
different RL algorithms or different approaches for predicting rewards. Project
website: https://robofume.github.io | [
"Jingyun Yang",
"Max Sobol Mark",
"Brandon Vu",
"Archit Sharma",
"Jeannette Bohg",
"Chelsea Finn"
] | 2023-10-23 17:50:08 | http://arxiv.org/abs/2310.15145v1 | http://arxiv.org/pdf/2310.15145v1 | 2310.15145v1 |
Hyperparameter optimization of hp-greedy reduced basis for gravitational wave surrogates | In a previous work we introduced, in the context of gravitational wave
science, an initial study on an automated domain-decomposition approach for
reduced basis through hp-greedy refinement. The approach constructs local
reduced bases of lower dimensionality than global ones, with the same or higher
accuracy. These ``light'' local bases should imply both faster evaluations when
predicting new waveforms and faster data analysis, in particular faster
statistical inference (the forward and inverse problems, respectively). In this
approach, however, we have previously found important dependence on several
hyperparameters, which do not appear in global reduced basis. This naturally
leads to the problem of hyperparameter optimization (HPO), which is the subject
of this paper. We tackle the problem through a Bayesian optimization, and show
its superiority when compared to grid or random searches. We find that for
gravitational waves from the collision of two spinning but non-precessing black
holes, for the same accuracy, local hp-greedy reduced bases with HPO have a
lower dimensionality of up to $4 \times$ for the cases here studied, depending
on the desired accuracy. This factor should directly translate in a parameter
estimation speedup, for instance. Such acceleration might help in the near
real-time requirements for electromagnetic counterparts of gravitational waves
from compact binary coalescences. In addition, we find that the Bayesian
approach used in this paper for HPO is two orders of magnitude faster than, for
example, a grid search, with about a $100 \times$ acceleration. The code
developed for this project is available as open source from public
repositories. | [
"Franco Cerino",
"Andrés Diaz-Pace",
"Emmanuel Tassone",
"Manuel Tiglio",
"Atuel Villegas"
] | 2023-10-23 17:48:11 | http://arxiv.org/abs/2310.15143v1 | http://arxiv.org/pdf/2310.15143v1 | 2310.15143v1 |
SpecTr: Fast Speculative Decoding via Optimal Transport | Autoregressive sampling from large language models has led to
state-of-the-art results in several natural language tasks. However,
autoregressive sampling generates tokens one at a time making it slow, and even
prohibitive in certain tasks. One way to speed up sampling is
$\textit{speculative decoding}$: use a small model to sample a $\textit{draft}$
(block or sequence of tokens), and then score all tokens in the draft by the
large language model in parallel. A subset of the tokens in the draft are
accepted (and the rest rejected) based on a statistical method to guarantee
that the final output follows the distribution of the large model. In this
work, we provide a principled understanding of speculative decoding through the
lens of optimal transport (OT) with $\textit{membership cost}$. This framework
can be viewed as an extension of the well-known $\textit{maximal-coupling}$
problem. This new formulation enables us to generalize the speculative decoding
method to allow for a set of $k$ candidates at the token-level, which leads to
an improved optimal membership cost. We show that the optimal draft selection
algorithm (transport plan) can be computed via linear programming, whose
best-known runtime is exponential in $k$. We then propose a valid draft
selection algorithm whose acceptance probability is $(1-1/e)$-optimal
multiplicatively. Moreover, it can be computed in time almost linear with size
of domain of a single token. Using this $new draft selection$ algorithm, we
develop a new autoregressive sampling algorithm called $\textit{SpecTr}$, which
provides speedup in decoding while ensuring that there is no quality
degradation in the decoded output. We experimentally demonstrate that for
state-of-the-art large language models, the proposed approach achieves a wall
clock speedup of 2.13X, a further 1.37X speedup over speculative decoding on
standard benchmarks. | [
"Ziteng Sun",
"Ananda Theertha Suresh",
"Jae Hun Ro",
"Ahmad Beirami",
"Himanshu Jain",
"Felix Yu"
] | 2023-10-23 17:47:34 | http://arxiv.org/abs/2310.15141v1 | http://arxiv.org/pdf/2310.15141v1 | 2310.15141v1 |
AutoDAN: Automatic and Interpretable Adversarial Attacks on Large Language Models | Safety alignment of Large Language Models (LLMs) can be compromised with
manual jailbreak attacks and (automatic) adversarial attacks. Recent work
suggests that patching LLMs against these attacks is possible: manual jailbreak
attacks are human-readable but often limited and public, making them easy to
block; adversarial attacks generate gibberish prompts that can be detected
using perplexity-based filters. In this paper, we show that these solutions may
be too optimistic. We propose an interpretable adversarial attack,
\texttt{AutoDAN}, that combines the strengths of both types of attacks. It
automatically generates attack prompts that bypass perplexity-based filters
while maintaining a high attack success rate like manual jailbreak attacks.
These prompts are interpretable and diverse, exhibiting strategies commonly
used in manual jailbreak attacks, and transfer better than their non-readable
counterparts when using limited training data or a single proxy model. We also
customize \texttt{AutoDAN}'s objective to leak system prompts, another
jailbreak application not addressed in the adversarial attack literature. %,
demonstrating the versatility of the approach. We can also customize the
objective of \texttt{AutoDAN} to leak system prompts, beyond the ability to
elicit harmful content from the model, demonstrating the versatility of the
approach. Our work provides a new way to red-team LLMs and to understand the
mechanism of jailbreak attacks. | [
"Sicheng Zhu",
"Ruiyi Zhang",
"Bang An",
"Gang Wu",
"Joe Barrow",
"Zichao Wang",
"Furong Huang",
"Ani Nenkova",
"Tong Sun"
] | 2023-10-23 17:46:07 | http://arxiv.org/abs/2310.15140v1 | http://arxiv.org/pdf/2310.15140v1 | 2310.15140v1 |
Quantifying the Dialect Gap and its Correlates Across Languages | Historically, researchers and consumers have noticed a decrease in quality
when applying NLP tools to minority variants of languages (i.e. Puerto Rican
Spanish or Swiss German), but studies exploring this have been limited to a
select few languages. Additionally, past studies have mainly been conducted in
a monolingual context, so cross-linguistic trends have not been identified and
tied to external factors. In this work, we conduct a comprehensive evaluation
of the most influential, state-of-the-art large language models (LLMs) across
two high-use applications, machine translation and automatic speech
recognition, to assess their functionality on the regional dialects of several
high- and low-resource languages. Additionally, we analyze how the regional
dialect gap is correlated with economic, social, and linguistic factors. The
impact of training data, including related factors like dataset size and its
construction procedure, is shown to be significant but not consistent across
models or languages, meaning a one-size-fits-all approach cannot be taken in
solving the dialect gap. This work will lay the foundation for furthering the
field of dialectal NLP by laying out evident disparities and identifying
possible pathways for addressing them through mindful data collection. | [
"Anjali Kantharuban",
"Ivan Vulić",
"Anna Korhonen"
] | 2023-10-23 17:42:01 | http://arxiv.org/abs/2310.15135v1 | http://arxiv.org/pdf/2310.15135v1 | 2310.15135v1 |
Location-Aware Visual Question Generation with Lightweight Models | This work introduces a novel task, location-aware visual question generation
(LocaVQG), which aims to generate engaging questions from data relevant to a
particular geographical location. Specifically, we represent such
location-aware information with surrounding images and a GPS coordinate. To
tackle this task, we present a dataset generation pipeline that leverages GPT-4
to produce diverse and sophisticated questions. Then, we aim to learn a
lightweight model that can address the LocaVQG task and fit on an edge device,
such as a mobile phone. To this end, we propose a method which can reliably
generate engaging questions from location-aware information. Our proposed
method outperforms baselines regarding human evaluation (e.g., engagement,
grounding, coherence) and automatic evaluation metrics (e.g., BERTScore,
ROUGE-2). Moreover, we conduct extensive ablation studies to justify our
proposed techniques for both generating the dataset and solving the task. | [
"Nicholas Collin Suwono",
"Justin Chih-Yao Chen",
"Tun Min Hung",
"Ting-Hao Kenneth Huang",
"I-Bin Liao",
"Yung-Hui Li",
"Lun-Wei Ku",
"Shao-Hua Sun"
] | 2023-10-23 17:33:31 | http://arxiv.org/abs/2310.15129v1 | http://arxiv.org/pdf/2310.15129v1 | 2310.15129v1 |
Projected Stochastic Gradient Descent with Quantum Annealed Binary Gradients | We present, QP-SBGD, a novel layer-wise stochastic optimiser tailored towards
training neural networks with binary weights, known as binary neural networks
(BNNs), on quantum hardware. BNNs reduce the computational requirements and
energy consumption of deep learning models with minimal loss in accuracy.
However, training them in practice remains to be an open challenge. Most known
BNN-optimisers either rely on projected updates or binarise weights
post-training. Instead, QP-SBGD approximately maps the gradient onto binary
variables, by solving a quadratic constrained binary optimisation. Under
practically reasonable assumptions, we show that this update rule converges
with a rate of $\mathcal{O}(1 / \sqrt{T})$. Moreover, we show how the
$\mathcal{NP}$-hard projection can be effectively executed on an adiabatic
quantum annealer, harnessing recent advancements in quantum computation. We
also introduce a projected version of this update rule and prove that if a
fixed point exists in the binary variable space, the modified updates will
converge to it. Last but not least, our algorithm is implemented layer-wise,
making it suitable to train larger networks on resource-limited quantum
hardware. Through extensive evaluations, we show that QP-SBGD outperforms or is
on par with competitive and well-established baselines such as BinaryConnect,
signSGD and ProxQuant when optimising the Rosenbrock function, training BNNs as
well as binary graph neural networks. | [
"Maximilian Krahn",
"Michelle Sasdelli",
"Fengyi Yang",
"Vladislav Golyanik",
"Juho Kannala",
"Tat-Jun Chin",
"Tolga Birdal"
] | 2023-10-23 17:32:38 | http://arxiv.org/abs/2310.15128v1 | http://arxiv.org/pdf/2310.15128v1 | 2310.15128v1 |
Open-Ended Instructable Embodied Agents with Memory-Augmented Large Language Models | Pre-trained and frozen LLMs can effectively map simple scene re-arrangement
instructions to programs over a robot's visuomotor functions through
appropriate few-shot example prompting. To parse open-domain natural language
and adapt to a user's idiosyncratic procedures, not known during prompt
engineering time, fixed prompts fall short. In this paper, we introduce HELPER,
an embodied agent equipped with an external memory of language-program pairs
that parses free-form human-robot dialogue into action programs through
retrieval-augmented LLM prompting: relevant memories are retrieved based on the
current dialogue, instruction, correction or VLM description, and used as
in-context prompt examples for LLM querying. The memory is expanded during
deployment to include pairs of user's language and action plans, to assist
future inferences and personalize them to the user's language and routines.
HELPER sets a new state-of-the-art in the TEACh benchmark in both Execution
from Dialog History (EDH) and Trajectory from Dialogue (TfD), with 1.7x
improvement over the previous SOTA for TfD. Our models, code and video results
can be found in our project's website: https://helper-agent-llm.github.io. | [
"Gabriel Sarch",
"Yue Wu",
"Michael J. Tarr",
"Katerina Fragkiadaki"
] | 2023-10-23 17:31:55 | http://arxiv.org/abs/2310.15127v1 | http://arxiv.org/pdf/2310.15127v1 | 2310.15127v1 |
Mixed-Variable Global Sensitivity Analysis For Knowledge Discovery And Efficient Combinatorial Materials Design | Global Sensitivity Analysis (GSA) is the study of the influence of any given
inputs on the outputs of a model. In the context of engineering design, GSA has
been widely used to understand both individual and collective contributions of
design variables on the design objectives. So far, global sensitivity studies
have often been limited to design spaces with only quantitative (numerical)
design variables. However, many engineering systems also contain, if not only,
qualitative (categorical) design variables in addition to quantitative design
variables. In this paper, we integrate Latent Variable Gaussian Process (LVGP)
with Sobol' analysis to develop the first metamodel-based mixed-variable GSA
method. Through numerical case studies, we validate and demonstrate the
effectiveness of our proposed method for mixed-variable problems. Furthermore,
while the proposed GSA method is general enough to benefit various engineering
design applications, we integrate it with multi-objective Bayesian optimization
(BO) to create a sensitivity-aware design framework in accelerating the Pareto
front design exploration for metal-organic framework (MOF) materials with
many-level combinatorial design spaces. Although MOFs are constructed only from
qualitative variables that are notoriously difficult to design, our method can
utilize sensitivity analysis to navigate the optimization in the many-level
large combinatorial design space, greatly expediting the exploration of novel
MOF candidates. | [
"Yigitcan Comlek",
"Liwei Wang",
"Wei Chen"
] | 2023-10-23 17:29:53 | http://arxiv.org/abs/2310.15124v1 | http://arxiv.org/pdf/2310.15124v1 | 2310.15124v1 |
Branch-Solve-Merge Improves Large Language Model Evaluation and Generation | Large Language Models (LLMs) are frequently used for multi-faceted language
generation and evaluation tasks that involve satisfying intricate user
constraints or taking into account multiple aspects and criteria. However,
their performance can fall short, due to the model's lack of coherence and
inability to plan and decompose the problem. We propose Branch-Solve-Merge
(BSM), a Large Language Model program (Schlag et al., 2023) for tackling such
challenging natural language tasks. It consists of branch, solve, and merge
modules that are parameterized with specific prompts to the base LLM. These
three modules plan a decomposition of the task into multiple parallel
sub-tasks, independently solve them, and fuse the solutions to the sub-tasks.
We apply our method to the tasks of LLM response evaluation and constrained
text generation and evaluate its effectiveness with multiple LLMs, including
Vicuna, LLaMA-2-chat, and GPT-4. BSM improves the evaluation correctness and
consistency for each LLM by enhancing human-LLM agreement by up to 26%,
reducing length and pairwise position biases by up to 50%, and allowing
LLaMA-2-chat to match or outperform GPT-4 on most domains. On the constraint
story generation task, BSM improves the coherence of the stories while also
improving constraint satisfaction by 12%. | [
"Swarnadeep Saha",
"Omer Levy",
"Asli Celikyilmaz",
"Mohit Bansal",
"Jason Weston",
"Xian Li"
] | 2023-10-23 17:29:48 | http://arxiv.org/abs/2310.15123v1 | http://arxiv.org/pdf/2310.15123v1 | 2310.15123v1 |
Matryoshka Diffusion Models | Diffusion models are the de facto approach for generating high-quality images
and videos, but learning high-dimensional models remains a formidable task due
to computational and optimization challenges. Existing methods often resort to
training cascaded models in pixel space or using a downsampled latent space of
a separately trained auto-encoder. In this paper, we introduce Matryoshka
Diffusion Models(MDM), an end-to-end framework for high-resolution image and
video synthesis. We propose a diffusion process that denoises inputs at
multiple resolutions jointly and uses a NestedUNet architecture where features
and parameters for small-scale inputs are nested within those of large scales.
In addition, MDM enables a progressive training schedule from lower to higher
resolutions, which leads to significant improvements in optimization for
high-resolution generation. We demonstrate the effectiveness of our approach on
various benchmarks, including class-conditioned image generation,
high-resolution text-to-image, and text-to-video applications. Remarkably, we
can train a single pixel-space model at resolutions of up to 1024x1024 pixels,
demonstrating strong zero-shot generalization using the CC12M dataset, which
contains only 12 million images. | [
"Jiatao Gu",
"Shuangfei Zhai",
"Yizhe Zhang",
"Josh Susskind",
"Navdeep Jaitly"
] | 2023-10-23 17:20:01 | http://arxiv.org/abs/2310.15111v1 | http://arxiv.org/pdf/2310.15111v1 | 2310.15111v1 |
Evaluating machine learning models in non-standard settings: An overview and new findings | Estimating the generalization error (GE) of machine learning models is
fundamental, with resampling methods being the most common approach. However,
in non-standard settings, particularly those where observations are not
independently and identically distributed, resampling using simple random data
divisions may lead to biased GE estimates. This paper strives to present
well-grounded guidelines for GE estimation in various such non-standard
settings: clustered data, spatial data, unequal sampling probabilities, concept
drift, and hierarchically structured outcomes. Our overview combines
well-established methodologies with other existing methods that, to our
knowledge, have not been frequently considered in these particular settings. A
unifying principle among these techniques is that the test data used in each
iteration of the resampling procedure should reflect the new observations to
which the model will be applied, while the training data should be
representative of the entire data set used to obtain the final model. Beyond
providing an overview, we address literature gaps by conducting simulation
studies. These studies assess the necessity of using GE-estimation methods
tailored to the respective setting. Our findings corroborate the concern that
standard resampling methods often yield biased GE estimates in non-standard
settings, underscoring the importance of tailored GE estimation. | [
"Roman Hornung",
"Malte Nalenz",
"Lennart Schneider",
"Andreas Bender",
"Ludwig Bothmann",
"Bernd Bischl",
"Thomas Augustin",
"Anne-Laure Boulesteix"
] | 2023-10-23 17:15:11 | http://arxiv.org/abs/2310.15108v1 | http://arxiv.org/pdf/2310.15108v1 | 2310.15108v1 |
Dual-path convolutional neural network using micro-FTIR imaging to predict breast cancer subtypes and biomarkers levels: estrogen receptor, progesterone receptor, HER2 and Ki67 | Breast cancer molecular subtypes classification plays an import role to sort
patients with divergent prognosis. The biomarkers used are Estrogen Receptor
(ER), Progesterone Receptor (PR), HER2, and Ki67. Based on these biomarkers
expression levels, subtypes are classified as Luminal A (LA), Luminal B (LB),
HER2 subtype, and Triple-Negative Breast Cancer (TNBC). Immunohistochemistry is
used to classify subtypes, although interlaboratory and interobserver
variations can affect its accuracy, besides being a time-consuming technique.
The Fourier transform infrared micro-spectroscopy may be coupled with deep
learning for cancer evaluation, where there is still a lack of studies for
subtypes and biomarker levels prediction. This study presents a novel 2D deep
learning approach to achieve these predictions. Sixty micro-FTIR images of
320x320 pixels were collected from a human breast biopsies microarray. Data
were clustered by K-means, preprocessed and 32x32 patches were generated using
a fully automated approach. CaReNet-V2, a novel convolutional neural network,
was developed to classify breast cancer (CA) vs adjacent tissue (AT) and
molecular subtypes, and to predict biomarkers level. The clustering method
enabled to remove non-tissue pixels. Test accuracies for CA vs AT and subtype
were above 0.84. The model enabled the prediction of ER, PR, and HER2 levels,
where borderline values showed lower performance (minimum accuracy of 0.54).
Ki67 percentage regression demonstrated a mean error of 3.6%. Thus, CaReNet-V2
is a potential technique for breast cancer biopsies evaluation, standing out as
a screening analysis technique and helping to prioritize patients. | [
"Matheus del-Valle",
"Emerson Soares Bernardes",
"Denise Maria Zezell"
] | 2023-10-23 17:05:53 | http://arxiv.org/abs/2310.15099v1 | http://arxiv.org/pdf/2310.15099v1 | 2310.15099v1 |
A Canonical Data Transformation for Achieving Inter- and Within-group Fairness | Increases in the deployment of machine learning algorithms for applications
that deal with sensitive data have brought attention to the issue of fairness
in machine learning. Many works have been devoted to applications that require
different demographic groups to be treated fairly. However, algorithms that aim
to satisfy inter-group fairness (also called group fairness) may inadvertently
treat individuals within the same demographic group unfairly. To address this
issue, we introduce a formal definition of within-group fairness that maintains
fairness among individuals from within the same group. We propose a
pre-processing framework to meet both inter- and within-group fairness criteria
with little compromise in accuracy. The framework maps the feature vectors of
members from different groups to an inter-group-fair canonical domain before
feeding them into a scoring function. The mapping is constructed to preserve
the relative relationship between the scores obtained from the unprocessed
feature vectors of individuals from the same demographic group, guaranteeing
within-group fairness. We apply this framework to the COMPAS risk assessment
and Law School datasets and compare its performance in achieving inter-group
and within-group fairness to two regularization-based methods. | [
"Zachary McBride Lazri",
"Ivan Brugere",
"Xin Tian",
"Dana Dachman-Soled",
"Antigoni Polychroniadou",
"Danial Dervovic",
"Min Wu"
] | 2023-10-23 17:00:20 | http://arxiv.org/abs/2310.15097v1 | http://arxiv.org/pdf/2310.15097v1 | 2310.15097v1 |
One-dimensional convolutional neural network model for breast cancer subtypes classification and biochemical content evaluation using micro-FTIR hyperspectral images | Breast cancer treatment still remains a challenge, where molecular subtypes
classification plays a crucial role in selecting appropriate and specific
therapy. The four subtypes are Luminal A (LA), Luminal B (LB), HER2 subtype,
and Triple-Negative Breast Cancer (TNBC). Immunohistochemistry is the
gold-standard evaluation, although interobserver variations are reported and
molecular signatures identification is time-consuming. Fourier transform
infrared micro-spectroscopy with machine learning approaches have been used to
evaluate cancer samples, presenting biochemical-related explainability.
However, this explainability is harder when using deep learning. This study
created a 1D deep learning tool for breast cancer subtype evaluation and
biochemical contribution. Sixty hyperspectral images were acquired from a human
breast cancer microarray. K-Means clustering was applied to select tissue and
paraffin spectra. CaReNet-V1, a novel 1D convolutional neural network, was
developed to classify breast cancer (CA) and adjacent tissue (AT), and
molecular subtypes. A 1D adaptation of Grad-CAM was applied to assess the
biochemical impact to the classifications. CaReNet-V1 effectively classified CA
and AT (test accuracy of 0.89), as well as HER2 and TNBC subtypes (0.83 and
0.86), with greater difficulty for LA and LB (0.74 and 0.68). The model enabled
the evaluation of the most contributing wavenumbers to the predictions,
providing a direct relationship with the biochemical content. Therefore,
CaReNet-V1 and hyperspectral images is a potential approach for breast cancer
biopsies assessment, providing additional information to the pathology report.
Biochemical content impact feature may be used for other studies, such as
treatment efficacy evaluation and development new diagnostics and therapeutic
methods. | [
"Matheus del-Valle",
"Emerson Soares Bernardes",
"Denise Maria Zezell"
] | 2023-10-23 16:58:34 | http://arxiv.org/abs/2310.15094v1 | http://arxiv.org/pdf/2310.15094v1 | 2310.15094v1 |
On the Detection of Image-Scaling Attacks in Machine Learning | Image scaling is an integral part of machine learning and computer vision
systems. Unfortunately, this preprocessing step is vulnerable to so-called
image-scaling attacks where an attacker makes unnoticeable changes to an image
so that it becomes a new image after scaling. This opens up new ways for
attackers to control the prediction or to improve poisoning and backdoor
attacks. While effective techniques exist to prevent scaling attacks, their
detection has not been rigorously studied yet. Consequently, it is currently
not possible to reliably spot these attacks in practice.
This paper presents the first in-depth systematization and analysis of
detection methods for image-scaling attacks. We identify two general detection
paradigms and derive novel methods from them that are simple in design yet
significantly outperform previous work. We demonstrate the efficacy of these
methods in a comprehensive evaluation with all major learning platforms and
scaling algorithms. First, we show that image-scaling attacks modifying the
entire scaled image can be reliably detected even under an adaptive adversary.
Second, we find that our methods provide strong detection performance even if
only minor parts of the image are manipulated. As a result, we can introduce a
novel protection layer against image-scaling attacks. | [
"Erwin Quiring",
"Andreas Müller",
"Konrad Rieck"
] | 2023-10-23 16:46:28 | http://arxiv.org/abs/2310.15085v1 | http://arxiv.org/pdf/2310.15085v1 | 2310.15085v1 |
Quantum Federated Learning With Quantum Networks | A major concern of deep learning models is the large amount of data that is
required to build and train them, much of which is reliant on sensitive and
personally identifiable information that is vulnerable to access by third
parties. Ideas of using the quantum internet to address this issue have been
previously proposed, which would enable fast and completely secure online
communications. Previous work has yielded a hybrid quantum-classical transfer
learning scheme for classical data and communication with a hub-spoke topology.
While quantum communication is secure from eavesdrop attacks and no
measurements from quantum to classical translation, due to no cloning theorem,
hub-spoke topology is not ideal for quantum communication without quantum
memory. Here we seek to improve this model by implementing a decentralized ring
topology for the federated learning scheme, where each client is given a
portion of the entire dataset and only performs training on that set. We also
demonstrate the first successful use of quantum weights for quantum federated
learning, which allows us to perform our training entirely in quantum. | [
"Tyler Wang",
"Huan-Hsin Tseng",
"Shinjae Yoo"
] | 2023-10-23 16:45:29 | http://arxiv.org/abs/2310.15084v1 | http://arxiv.org/pdf/2310.15084v1 | 2310.15084v1 |
Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization | Federated learning (FL) is a promising paradigm to enable collaborative model
training with decentralized data. However, the training process of Large
Language Models (LLMs) generally incurs the update of significant parameters,
which limits the applicability of FL techniques to tackle the LLMs in real
scenarios. Prompt tuning can significantly reduce the number of parameters to
update, but it either incurs performance degradation or low training
efficiency. The straightforward utilization of prompt tuning in the FL often
raises non-trivial communication costs and dramatically degrades performance.
In addition, the decentralized data is generally non-Independent and
Identically Distributed (non-IID), which brings client drift problems and thus
poor performance. This paper proposes a Parameter-efficient prompt Tuning
approach with Adaptive Optimization, i.e., FedPepTAO, to enable efficient and
effective FL of LLMs. First, an efficient partial prompt tuning approach is
proposed to improve performance and efficiency simultaneously. Second, a novel
adaptive optimization method is developed to address the client drift problems
on both the device and server sides to enhance performance further. Extensive
experiments based on 10 datasets demonstrate the superb performance (up to
60.8\% in terms of accuracy) and efficiency (up to 97.59\% in terms of training
time) of FedPepTAO compared with 9 baseline approaches. Our code is available
at https://github.com/llm-eff/FedPepTAO. | [
"Tianshi Che",
"Ji Liu",
"Yang Zhou",
"Jiaxiang Ren",
"Jiwen Zhou",
"Victor S. Sheng",
"Huaiyu Dai",
"Dejing Dou"
] | 2023-10-23 16:37:59 | http://arxiv.org/abs/2310.15080v1 | http://arxiv.org/pdf/2310.15080v1 | 2310.15080v1 |
MGAS: Multi-Granularity Architecture Search for Effective and Efficient Neural Networks | Differentiable architecture search (DAS) has become the prominent approach in
the field of neural architecture search (NAS) due to its time-efficient
automation of neural network design. It shifts the traditional paradigm of
discrete architecture sampling and evaluation to differentiable super-net
optimization and discretization. However, existing DAS methods either only
conduct coarse-grained operation-level search, or restrictively explore
fine-grained filter-level and weight-level units using manually-defined
remaining ratios, which fail to simultaneously achieve small model size and
satisfactory model performance. Additionally, they address the high memory
consumption of the search process at the expense of search quality. To tackle
these issues, we introduce multi-granularity architecture search (MGAS), a
unified framework which aims to comprehensively and memory-efficiently explore
the multi-granularity search space to discover both effective and efficient
neural networks. Specifically, we learn discretization functions specific to
each granularity level to adaptively determine the remaining ratios according
to the evolving architecture. This ensures an optimal balance among units of
different granularity levels for different target model sizes. Considering the
memory demands, we break down the super-net optimization and discretization
into multiple sub-net stages. By allowing re-pruning and regrowing of units in
previous sub-nets during subsequent stages, we compensate for potential bias in
earlier stages. Extensive experiments on CIFAR-10, CIFAR-100 and ImageNet
demonstrate that MGAS outperforms other state-of-the-art methods in achieving a
better trade-off between model performance and model size. | [
"Xiaoyun Liu",
"Divya Saxena",
"Jiannong Cao",
"Yuqing Zhao",
"Penghui Ruan"
] | 2023-10-23 16:32:18 | http://arxiv.org/abs/2310.15074v1 | http://arxiv.org/pdf/2310.15074v1 | 2310.15074v1 |
Robot Skill Generalization via Keypoint Integrated Soft Actor-Critic Gaussian Mixture Models | A long-standing challenge for a robotic manipulation system operating in
real-world scenarios is adapting and generalizing its acquired motor skills to
unseen environments. We tackle this challenge employing hybrid skill models
that integrate imitation and reinforcement paradigms, to explore how the
learning and adaptation of a skill, along with its core grounding in the scene
through a learned keypoint, can facilitate such generalization. To that end, we
develop Keypoint Integrated Soft Actor-Critic Gaussian Mixture Models (KIS-GMM)
approach that learns to predict the reference of a dynamical system within the
scene as a 3D keypoint, leveraging visual observations obtained by the robot's
physical interactions during skill learning. Through conducting comprehensive
evaluations in both simulated and real-world environments, we show that our
method enables a robot to gain a significant zero-shot generalization to novel
environments and to refine skills in the target environments faster than
learning from scratch. Importantly, this is achieved without the need for new
ground truth data. Moreover, our method effectively copes with scene
displacements. | [
"Iman Nematollahi",
"Kirill Yankov",
"Wolfram Burgard",
"Tim Welschehold"
] | 2023-10-23 16:03:23 | http://arxiv.org/abs/2310.15059v1 | http://arxiv.org/pdf/2310.15059v1 | 2310.15059v1 |
Coordinated Replay Sample Selection for Continual Federated Learning | Continual Federated Learning (CFL) combines Federated Learning (FL), the
decentralized learning of a central model on a number of client devices that
may not communicate their data, and Continual Learning (CL), the learning of a
model from a continual stream of data without keeping the entire history. In
CL, the main challenge is \textit{forgetting} what was learned from past data.
While replay-based algorithms that keep a small pool of past training data are
effective to reduce forgetting, only simple replay sample selection strategies
have been applied to CFL in prior work, and no previous work has explored
coordination among clients for better sample selection. To bridge this gap, we
adapt a replay sample selection objective based on loss gradient diversity to
CFL and propose a new relaxation-based selection of samples to optimize the
objective. Next, we propose a practical algorithm to coordinate gradient-based
replay sample selection across clients without communicating private data. We
benchmark our coordinated and uncoordinated replay sample selection algorithms
against random sampling-based baselines with language models trained on a large
scale de-identified real-world text dataset. We show that gradient-based sample
selection methods both boost performance and reduce forgetting compared to
random sampling methods, with our coordination method showing gains early in
the low replay size regime (when the budget for storing past data is small). | [
"Jack Good",
"Jimit Majmudar",
"Christophe Dupuy",
"Jixuan Wang",
"Charith Peris",
"Clement Chung",
"Richard Zemel",
"Rahul Gupta"
] | 2023-10-23 15:56:39 | http://arxiv.org/abs/2310.15054v1 | http://arxiv.org/pdf/2310.15054v1 | 2310.15054v1 |
TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge | We introduce TeleQnA, the first benchmark dataset designed to evaluate the
knowledge of Large Language Models (LLMs) in telecommunications. Comprising
10,000 questions and answers, this dataset draws from diverse sources,
including standards and research articles. This paper outlines the automated
question generation framework responsible for creating this dataset, along with
how human input was integrated at various stages to ensure the quality of the
questions. Afterwards, using the provided dataset, an evaluation is conducted
to assess the capabilities of LLMs, including GPT-3.5 and GPT-4. The results
highlight that these models struggle with complex standards related questions
but exhibit proficiency in addressing general telecom-related inquiries.
Additionally, our results showcase how incorporating telecom knowledge context
significantly enhances their performance, thus shedding light on the need for a
specialized telecom foundation model. Finally, the dataset is shared with
active telecom professionals, whose performance is subsequently benchmarked
against that of the LLMs. The findings illustrate that LLMs can rival the
performance of active professionals in telecom knowledge, thanks to their
capacity to process vast amounts of information, underscoring the potential of
LLMs within this domain. The dataset has been made publicly accessible on
GitHub. | [
"Ali Maatouk",
"Fadhel Ayed",
"Nicola Piovesan",
"Antonio De Domenico",
"Merouane Debbah",
"Zhi-Quan Luo"
] | 2023-10-23 15:55:15 | http://arxiv.org/abs/2310.15051v1 | http://arxiv.org/pdf/2310.15051v1 | 2310.15051v1 |
Meta- (out-of-context) learning in neural networks | Brown et al. (2020) famously introduced the phenomenon of in-context learning
in large language models (LLMs). We establish the existence of a phenomenon we
call $\textbf{meta-out-of-context learning (meta-OCL)}$ via carefully designed
synthetic experiments with LLMs. Our results suggest that meta-OCL leads LLMs
to more readily "internalize" the semantic content of text that is, or appears
to be, broadly useful (such as true statements, or text from authoritative
sources) and use it in appropriate circumstances. We further demonstrate
meta-OCL in a synthetic computer vision setting, and propose two hypotheses for
the emergence of meta-OCL: one relying on the way models store knowledge in
their parameters, and another suggesting that the implicit gradient alignment
bias of gradient-descent-based optimizers may be responsible. Finally, we
reflect on what our results might imply about capabilities of future AI
systems, and discuss potential risks. Our code can be found at
https://github.com/krasheninnikov/internalization . | [
"Dmitrii Krasheninnikov",
"Egor Krasheninnikov",
"Bruno Mlodozeniec",
"David Krueger"
] | 2023-10-23 15:50:08 | http://arxiv.org/abs/2310.15047v1 | http://arxiv.org/pdf/2310.15047v1 | 2310.15047v1 |
Deep Autoencoder-based Z-Interference Channels with Perfect and Imperfect CSI | A deep autoencoder (DAE)-based structure for endto-end communication over the
two-user Z-interference channel (ZIC) with finite-alphabet inputs is designed
in this paper. The proposed structure jointly optimizes the two encoder/decoder
pairs and generates interference-aware constellations that dynamically adapt
their shape based on interference intensity to minimize the bit error rate
(BER). An in-phase/quadrature-phase (I/Q) power allocation layer is introduced
in the DAE to guarantee an average power constraint and enable the architecture
to generate constellations with nonuniform shapes. This brings further gain
compared to standard uniform constellations such as quadrature amplitude
modulation. The proposed structure is then extended to work with imperfect
channel state information (CSI). The CSI imperfection due to both the
estimation and quantization errors are examined. The performance of the DAEZIC
is compared with two baseline methods, i.e., standard and rotated
constellations. The proposed structure significantly enhances the performance
of the ZIC both for the perfect and imperfect CSI. Simulation results show that
the improvement is achieved in all interference regimes (weak, moderate, and
strong) and consistently increases with the signal-to-noise ratio (SNR). For
example, more than an order of magnitude BER reduction is obtained with respect
to the most competitive conventional method at weak interference when SNR>15dB
and two bits per symbol are transmitted. The improvements reach about two
orders of magnitude when quantization error exists, indicating that the DAE-ZIC
is more robust to the interference compared to the conventional methods. | [
"Xinliang Zhang",
"Mojtaba Vaezi"
] | 2023-10-23 15:23:42 | http://arxiv.org/abs/2310.15027v1 | http://arxiv.org/pdf/2310.15027v1 | 2310.15027v1 |
Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data | High-energy large-scale particle colliders produce data at high speed in the
order of 1 terabytes per second in nuclear physics and petabytes per second in
high-energy physics. Developing real-time data compression algorithms to reduce
such data at high throughput to fit permanent storage has drawn increasing
attention. Specifically, at the newly constructed sPHENIX experiment at the
Relativistic Heavy Ion Collider (RHIC), a time projection chamber is used as
the main tracking detector, which records particle trajectories in a volume of
a three-dimensional (3D) cylinder. The resulting data are usually very sparse
with occupancy around 10.8%. Such sparsity presents a challenge to conventional
learning-free lossy compression algorithms, such as SZ, ZFP, and MGARD. The 3D
convolutional neural network (CNN)-based approach, Bicephalous Convolutional
Autoencoder (BCAE), outperforms traditional methods both in compression rate
and reconstruction accuracy. BCAE can also utilize the computation power of
graphical processing units suitable for deployment in a modern heterogeneous
high-performance computing environment. This work introduces two BCAE variants:
BCAE++ and BCAE-2D. BCAE++ achieves a 15% better compression ratio and a 77%
better reconstruction accuracy measured in mean absolute error compared with
BCAE. BCAE-2D treats the radial direction as the channel dimension of an image,
resulting in a 3x speedup in compression throughput. In addition, we
demonstrate an unbalanced autoencoder with a larger decoder can improve
reconstruction accuracy without significantly sacrificing throughput. Lastly,
we observe both the BCAE++ and BCAE-2D can benefit more from using
half-precision mode in throughput (76-79% increase) without loss in
reconstruction accuracy. The source code and links to data and pretrained
models can be found at https://github.com/BNL-DAQ-LDRD/NeuralCompression_v2. | [
"Yi Huang",
"Yihui Ren",
"Shinjae Yoo",
"Jin Huang"
] | 2023-10-23 15:23:32 | http://arxiv.org/abs/2310.15026v1 | http://arxiv.org/pdf/2310.15026v1 | 2310.15026v1 |
Invariance is Key to Generalization: Examining the Role of Representation in Sim-to-Real Transfer for Visual Navigation | The data-driven approach to robot control has been gathering pace rapidly,
yet generalization to unseen task domains remains a critical challenge. We
argue that the key to generalization is representations that are (i) rich
enough to capture all task-relevant information and (ii) invariant to
superfluous variability between the training and the test domains. We
experimentally study such a representation -- containing both depth and
semantic information -- for visual navigation and show that it enables a
control policy trained entirely in simulated indoor scenes to generalize to
diverse real-world environments, both indoors and outdoors. Further, we show
that our representation reduces the A-distance between the training and test
domains, improving the generalization error bound as a result. Our proposed
approach is scalable: the learned policy improves continuously, as the
foundation models that it exploits absorb more diverse data during
pre-training. | [
"Bo Ai",
"Zhanxin Wu",
"David Hsu"
] | 2023-10-23 15:15:19 | http://arxiv.org/abs/2310.15020v1 | http://arxiv.org/pdf/2310.15020v1 | 2310.15020v1 |
Meta learning with language models: Challenges and opportunities in the classification of imbalanced text | Detecting out of policy speech (OOPS) content is important but difficult.
While machine learning is a powerful tool to tackle this challenging task, it
is hard to break the performance ceiling due to factors like quantity and
quality limitations on training data and inconsistencies in OOPS definition and
data labeling. To realize the full potential of available limited resources, we
propose a meta learning technique (MLT) that combines individual models built
with different text representations. We analytically show that the resulting
technique is numerically stable and produces reasonable combining weights. We
combine the MLT with a threshold-moving (TM) technique to further improve the
performance of the combined predictor on highly-imbalanced in-distribution and
out-of-distribution datasets. We also provide computational results to show the
statistically significant advantages of the proposed MLT approach.
All authors contributed equally to this work. | [
"Apostol Vassilev",
"Honglan Jin",
"Munawar Hasan"
] | 2023-10-23 15:14:55 | http://arxiv.org/abs/2310.15019v1 | http://arxiv.org/pdf/2310.15019v1 | 2310.15019v1 |
The primacy bias in Model-based RL | The primacy bias in deep reinforcement learning (DRL), which refers to the
agent's tendency to overfit early data and lose the ability to learn from new
data, can significantly decrease the performance of DRL algorithms. Previous
studies have shown that employing simple techniques, such as resetting the
agent's parameters, can substantially alleviate the primacy bias. However, we
observe that resetting the agent's parameters harms its performance in the
context of model-based reinforcement learning (MBRL). In fact, on further
investigation, we find that the primacy bias in MBRL differs from that in
model-free RL. In this work, we focus on investigating the primacy bias in MBRL
and propose world model resetting, which works in MBRL. We apply our method to
two different MBRL algorithms, MBPO and DreamerV2. We validate the
effectiveness of our method on multiple continuous control tasks on MuJoCo and
DeepMind Control Suite, as well as discrete control tasks on Atari 100k
benchmark. The results show that world model resetting can significantly
alleviate the primacy bias in model-based setting and improve algorithm's
performance. We also give a guide on how to perform world model resetting
effectively. | [
"Zhongjian Qiao",
"Jiafei Lyu",
"Xiu Li"
] | 2023-10-23 15:12:20 | http://arxiv.org/abs/2310.15017v1 | http://arxiv.org/pdf/2310.15017v1 | 2310.15017v1 |
Leveraging Deep Learning for Abstractive Code Summarization of Unofficial Documentation | Usually, programming languages have official documentation to guide
developers with APIs, methods, and classes. However, researchers identified
insufficient or inadequate documentation examples and flaws with the API's
complex structure as barriers to learning an API. As a result, developers may
consult other sources (StackOverflow, GitHub, etc.) to learn more about an API.
Recent research studies have shown that unofficial documentation is a valuable
source of information for generating code summaries. We, therefore, have been
motivated to leverage such a type of documentation along with deep learning
techniques towards generating high-quality summaries for APIs discussed in
informal documentation.
This paper proposes an automatic approach using the BART algorithm, a
state-of-the-art transformer model, to generate summaries for APIs discussed in
StackOverflow. We built an oracle of human-generated summaries to evaluate our
approach against it using ROUGE and BLEU metrics which are the most widely used
evaluation metrics in text summarization. Furthermore, we evaluated our
summaries empirically against a previous work in terms of quality. Our findings
demonstrate that using deep learning algorithms can improve summaries' quality
and outperform the previous work by an average of %57 for Precision, %66 for
Recall, and %61 for F-measure, and it runs 4.4 times faster. | [
"AmirHossein Naghshzan",
"Latifa Guerrouj",
"Olga Baysal"
] | 2023-10-23 15:10:37 | http://arxiv.org/abs/2310.15015v1 | http://arxiv.org/pdf/2310.15015v1 | 2310.15015v1 |
Did the Neurons Read your Book? Document-level Membership Inference for Large Language Models | With large language models (LLMs) poised to become embedded in our daily
lives, questions are starting to be raised about the dataset(s) they learned
from. These questions range from potential bias or misinformation LLMs could
retain from their training data to questions of copyright and fair use of
human-generated text. However, while these questions emerge, developers of the
recent state-of-the-art LLMs become increasingly reluctant to disclose details
on their training corpus. We here introduce the task of document-level
membership inference for real-world LLMs, i.e. inferring whether the LLM has
seen a given document during training or not. First, we propose a procedure for
the development and evaluation of document-level membership inference for LLMs
by leveraging commonly used data sources for training and the model release
date. We then propose a practical, black-box method to predict document-level
membership and instantiate it on OpenLLaMA-7B with both books and academic
papers. We show our methodology to perform very well, reaching an impressive
AUC of 0.856 for books and 0.678 for papers. We then show our approach to
outperform the sentence-level membership inference attacks used in the privacy
literature for the document-level membership task. We finally evaluate whether
smaller models might be less sensitive to document-level inference and show
OpenLLaMA-3B to be approximately as sensitive as OpenLLaMA-7B to our approach.
Taken together, our results show that accurate document-level membership can be
inferred for LLMs, increasing the transparency of technology poised to change
our lives. | [
"Matthieu Meeus",
"Shubham Jain",
"Marek Rei",
"Yves-Alexandre de Montjoye"
] | 2023-10-23 15:00:46 | http://arxiv.org/abs/2310.15007v1 | http://arxiv.org/pdf/2310.15007v1 | 2310.15007v1 |
Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries | The inductive bias of a graph neural network (GNN) is largely encoded in its
specified graph. Latent graph inference relies on latent geometric
representations to dynamically rewire or infer a GNN's graph to maximize the
GNN's predictive downstream performance, but it lacks solid theoretical
foundations in terms of embedding-based representation guarantees. This paper
addresses this issue by introducing a trainable deep learning architecture,
coined neural snowflake, that can adaptively implement fractal-like metrics on
$\mathbb{R}^d$. We prove that any given finite weights graph can be
isometrically embedded by a standard MLP encoder. Furthermore, when the latent
graph can be represented in the feature space of a sufficiently regular kernel,
we show that the combined neural snowflake and MLP encoder do not succumb to
the curse of dimensionality by using only a low-degree polynomial number of
parameters in the number of nodes. This implementation enables a
low-dimensional isometric embedding of the latent graph. We conduct synthetic
experiments to demonstrate the superior metric learning capabilities of neural
snowflakes when compared to more familiar spaces like Euclidean space.
Additionally, we carry out latent graph inference experiments on graph
benchmarks. Consistently, the neural snowflake model achieves predictive
performance that either matches or surpasses that of the state-of-the-art
latent graph inference models. Importantly, this performance improvement is
achieved without requiring random search for optimal latent geometry. Instead,
the neural snowflake model achieves this enhancement in a differentiable
manner. | [
"Haitz Sáez de Ocáriz Borde",
"Anastasis Kratsios"
] | 2023-10-23 14:57:26 | http://arxiv.org/abs/2310.15003v1 | http://arxiv.org/pdf/2310.15003v1 | 2310.15003v1 |
Simple Hardware-Efficient PCFGs with Independent Left and Right Productions | Scaling dense PCFGs to thousands of nonterminals via a low-rank
parameterization of the rule probability tensor has been shown to be beneficial
for unsupervised parsing. However, PCFGs scaled this way still perform poorly
as a language model, and even underperform similarly-sized HMMs. This work
introduces \emph{SimplePCFG}, a simple PCFG formalism with independent left and
right productions. Despite imposing a stronger independence assumption than the
low-rank approach, we find that this formalism scales more effectively both as
a language model and as an unsupervised parser. As an unsupervised parser, our
simple PCFG obtains an average F1 of 65.1 on the English PTB, and as a language
model, it obtains a perplexity of 119.0, outperforming similarly-sized low-rank
PCFGs. We further introduce \emph{FlashInside}, a hardware IO-aware
implementation of the inside algorithm for efficiently scaling simple PCFGs. | [
"Wei Liu",
"Songlin Yang",
"Yoon Kim",
"Kewei Tu"
] | 2023-10-23 14:48:51 | http://arxiv.org/abs/2310.14997v1 | http://arxiv.org/pdf/2310.14997v1 | 2310.14997v1 |
Understanding the Inner Workings of Language Models Through Representation Dissimilarity | As language models are applied to an increasing number of real-world
applications, understanding their inner workings has become an important issue
in model trust, interpretability, and transparency. In this work we show that
representation dissimilarity measures, which are functions that measure the
extent to which two model's internal representations differ, can be a valuable
tool for gaining insight into the mechanics of language models. Among our
insights are: (i) an apparent asymmetry in the internal representations of
model using SoLU and GeLU activation functions, (ii) evidence that
dissimilarity measures can identify and locate generalization properties of
models that are invisible via in-distribution test set performance, and (iii)
new evaluations of how language model features vary as width and depth are
increased. Our results suggest that dissimilarity measures are a promising set
of tools for shedding light on the inner workings of language models. | [
"Davis Brown",
"Charles Godfrey",
"Nicholas Konz",
"Jonathan Tu",
"Henry Kvinge"
] | 2023-10-23 14:46:20 | http://arxiv.org/abs/2310.14993v1 | http://arxiv.org/pdf/2310.14993v1 | 2310.14993v1 |
Bayesian Regression Markets | Machine learning tasks are vulnerable to the quality of data used as input.
Yet, it is often challenging for firms to obtain adequate datasets, with them
being naturally distributed amongst owners, that in practice, may be
competitors in a downstream market and reluctant to share information. Focusing
on supervised learning for regression tasks, we develop a \textit{regression
market} to provide a monetary incentive for data sharing. Our proposed
mechanism adopts a Bayesian framework, allowing us to consider a more general
class of regression tasks. We present a thorough exploration of the market
properties, and show that similar proposals in current literature expose the
market agents to sizeable financial risks, which can be mitigated in our
probabilistic setting. | [
"Thomas Falconer",
"Jalal Kazempour",
"Pierre Pinson"
] | 2023-10-23 14:45:51 | http://arxiv.org/abs/2310.14992v1 | http://arxiv.org/pdf/2310.14992v1 | 2310.14992v1 |
Delayed Memory Unit: Modelling Temporal Dependency Through Delay Gate | Recurrent Neural Networks (RNNs) are renowned for their adeptness in modeling
temporal dependencies, a trait that has driven their widespread adoption for
sequential data processing. Nevertheless, vanilla RNNs are confronted with the
well-known issue of gradient vanishing and exploding, posing a significant
challenge for learning and establishing long-range dependencies. Additionally,
gated RNNs tend to be over-parameterized, resulting in poor network
generalization. To address these challenges, we propose a novel Delayed Memory
Unit (DMU) in this paper, wherein a delay line structure, coupled with delay
gates, is introduced to facilitate temporal interaction and temporal credit
assignment, so as to enhance the temporal modeling capabilities of vanilla
RNNs. Particularly, the DMU is designed to directly distribute the input
information to the optimal time instant in the future, rather than aggregating
and redistributing it over time through intricate network dynamics. Our
proposed DMU demonstrates superior temporal modeling capabilities across a
broad range of sequential modeling tasks, utilizing considerably fewer
parameters than other state-of-the-art gated RNN models in applications such as
speech recognition, radar gesture recognition, ECG waveform segmentation, and
permuted sequential image classification. | [
"Pengfei Sun",
"Jibin Wu",
"Malu Zhang",
"Paul Devos",
"Dick Botteldooren"
] | 2023-10-23 14:29:48 | http://arxiv.org/abs/2310.14982v1 | http://arxiv.org/pdf/2310.14982v1 | 2310.14982v1 |
ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation | Label aggregation such as majority voting is commonly used to resolve
annotator disagreement in dataset creation. However, this may disregard
minority values and opinions. Recent studies indicate that learning from
individual annotations outperforms learning from aggregated labels, though they
require a considerable amount of annotation. Active learning, as an annotation
cost-saving strategy, has not been fully explored in the context of learning
from disagreement. We show that in the active learning setting, a multi-head
model performs significantly better than a single-head model in terms of
uncertainty estimation. By designing and evaluating acquisition functions with
annotator-specific heads on two datasets, we show that group-level entropy
works generally well on both datasets. Importantly, it achieves performance in
terms of both prediction and uncertainty estimation comparable to full-scale
training from disagreement, while saving up to 70% of the annotation budget. | [
"Xinpeng Wang",
"Barbara Plank"
] | 2023-10-23 14:26:43 | http://arxiv.org/abs/2310.14979v1 | http://arxiv.org/pdf/2310.14979v1 | 2310.14979v1 |
Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation | Reinforcement learning (RL) has helped improve decision-making in several
applications. However, applying traditional RL is challenging in some
applications, such as rehabilitation of people with a spinal cord injury (SCI).
Among other factors, using RL in this domain is difficult because there are
many possible treatments (i.e., large action space) and few patients (i.e.,
limited training data). Treatments for SCIs have natural groupings, so we
propose two approaches to grouping treatments so that an RL agent can learn
effectively from limited data. One relies on domain knowledge of SCI
rehabilitation and the other learns similarities among treatments using an
embedding technique. We then use Fitted Q Iteration to train an agent that
learns optimal treatments. Through a simulation study designed to reflect the
properties of SCI rehabilitation, we find that both methods can help improve
the treatment decisions of physiotherapists, but the approach based on domain
knowledge offers better performance. Our findings provide a "proof of concept"
that RL can be used to help improve the treatment of those with an SCI and
indicates that continued efforts to gather data and apply RL to this domain are
worthwhile. | [
"Nathan Phelps",
"Stephanie Marrocco",
"Stephanie Cornell",
"Dalton L. Wolfe",
"Daniel J. Lizotte"
] | 2023-10-23 14:25:55 | http://arxiv.org/abs/2310.14976v1 | http://arxiv.org/pdf/2310.14976v1 | 2310.14976v1 |
The Fundamental Dilemma of Bayesian Active Meta-learning | Many applications involve estimation of parameters that generalize across
multiple diverse, but related, data-scarce task environments. Bayesian active
meta-learning, a form of sequential optimal experimental design, provides a
framework for solving such problems. The active meta-learner's goal is to gain
transferable knowledge (estimate the transferable parameters) in the presence
of idiosyncratic characteristics of the current task (task-specific
parameters). We show that in such a setting, greedy pursuit of this goal can
actually hurt estimation of the transferable parameters (induce so-called
negative transfer). The learner faces a dilemma akin to but distinct from the
exploration--exploitation dilemma: should they spend their acquisition budget
pursuing transferable knowledge, or identifying the current task-specific
parameters? We show theoretically that some tasks pose an inevitable and
arbitrarily large threat of negative transfer, and that task identification is
critical to reducing this threat. Our results generalize to analysis of prior
misspecification over nuisance parameters. Finally, we empirically illustrate
circumstances that lead to negative transfer. | [
"Sabina J. Sloman",
"Ayush Bharti",
"Samuel Kaski"
] | 2023-10-23 14:13:27 | http://arxiv.org/abs/2310.14968v1 | http://arxiv.org/pdf/2310.14968v1 | 2310.14968v1 |
Adam through a Second-Order Lens | Research into optimisation for deep learning is characterised by a tension
between the computational efficiency of first-order, gradient-based methods
(such as SGD and Adam) and the theoretical efficiency of second-order,
curvature-based methods (such as quasi-Newton methods and K-FAC). We seek to
combine the benefits of both approaches into a single computationally-efficient
algorithm. Noting that second-order methods often depend on stabilising
heuristics (such as Levenberg-Marquardt damping), we propose AdamQLR: an
optimiser combining damping and learning rate selection techniques from K-FAC
(Martens and Grosse, 2015) with the update directions proposed by Adam,
inspired by considering Adam through a second-order lens. We evaluate AdamQLR
on a range of regression and classification tasks at various scales, achieving
competitive generalisation performance vs runtime. | [
"Ross M. Clarke",
"Baiyu Su",
"José Miguel Hernández-Lobato"
] | 2023-10-23 14:06:46 | http://arxiv.org/abs/2310.14963v1 | http://arxiv.org/pdf/2310.14963v1 | 2310.14963v1 |
StenUNet: Automatic Stenosis Detection from X-ray Coronary Angiography | Coronary angiography continues to serve as the primary method for diagnosing
coronary artery disease (CAD), which is the leading global cause of mortality.
The severity of CAD is quantified by the location, degree of narrowing
(stenosis), and number of arteries involved. In current practice, this
quantification is performed manually using visual inspection and thus suffers
from poor inter- and intra-rater reliability. The MICCAI grand challenge:
Automatic Region-based Coronary Artery Disease diagnostics using the X-ray
angiography imagEs (ARCADE) curated a dataset with stenosis annotations, with
the goal of creating an automated stenosis detection algorithm. Using a
combination of machine learning and other computer vision techniques, we
propose the architecture and algorithm StenUNet to accurately detect stenosis
from X-ray Coronary Angiography. Our submission to the ARCADE challenge placed
3rd among all teams. We achieved an F1 score of 0.5348 on the test set, 0.0005
lower than the 2nd place. | [
"Hui Lin",
"Tom Liu",
"Aggelos Katsaggelos",
"Adrienne Kline"
] | 2023-10-23 14:04:18 | http://arxiv.org/abs/2310.14961v1 | http://arxiv.org/pdf/2310.14961v1 | 2310.14961v1 |
XTSC-Bench: Quantitative Benchmarking for Explainers on Time Series Classification | Despite the growing body of work on explainable machine learning in time
series classification (TSC), it remains unclear how to evaluate different
explainability methods. Resorting to qualitative assessment and user studies to
evaluate explainers for TSC is difficult since humans have difficulties
understanding the underlying information contained in time series data.
Therefore, a systematic review and quantitative comparison of explanation
methods to confirm their correctness becomes crucial. While steps to
standardized evaluations were taken for tabular, image, and textual data,
benchmarking explainability methods on time series is challenging due to a)
traditional metrics not being directly applicable, b) implementation and
adaption of traditional metrics for time series in the literature vary, and c)
varying baseline implementations. This paper proposes XTSC-Bench, a
benchmarking tool providing standardized datasets, models, and metrics for
evaluating explanation methods on TSC. We analyze 3 perturbation-, 6 gradient-
and 2 example-based explanation methods to TSC showing that improvements in the
explainers' robustness and reliability are necessary, especially for
multivariate data. | [
"Jacqueline Höllig",
"Steffen Thoma",
"Florian Grimm"
] | 2023-10-23 14:00:02 | http://arxiv.org/abs/2310.14957v1 | http://arxiv.org/pdf/2310.14957v1 | 2310.14957v1 |
Causal machine learning for single-cell genomics | Advances in single-cell omics allow for unprecedented insights into the
transcription profiles of individual cells. When combined with large-scale
perturbation screens, through which specific biological mechanisms can be
targeted, these technologies allow for measuring the effect of targeted
perturbations on the whole transcriptome. These advances provide an opportunity
to better understand the causative role of genes in complex biological
processes such as gene regulation, disease progression or cellular development.
However, the high-dimensional nature of the data, coupled with the intricate
complexity of biological systems renders this task nontrivial. Within the
machine learning community, there has been a recent increase of interest in
causality, with a focus on adapting established causal techniques and
algorithms to handle high-dimensional data. In this perspective, we delineate
the application of these methodologies within the realm of single-cell genomics
and their challenges. We first present the model that underlies most of current
causal approaches to single-cell biology and discuss and challenge the
assumptions it entails from the biological point of view. We then identify open
problems in the application of causal approaches to single-cell data:
generalising to unseen environments, learning interpretable models, and
learning causal models of dynamics. For each problem, we discuss how various
research directions - including the development of computational approaches and
the adaptation of experimental protocols - may offer ways forward, or on the
contrary pose some difficulties. With the advent of single cell atlases and
increasing perturbation data, we expect causal models to become a crucial tool
for informed experimental design. | [
"Alejandro Tejada-Lapuerta",
"Paul Bertin",
"Stefan Bauer",
"Hananeh Aliee",
"Yoshua Bengio",
"Fabian J. Theis"
] | 2023-10-23 13:35:24 | http://arxiv.org/abs/2310.14935v1 | http://arxiv.org/pdf/2310.14935v1 | 2310.14935v1 |
Robust Depth Linear Error Decomposition with Double Total Variation and Nuclear Norm for Dynamic MRI Reconstruction | Compressed Sensing (CS) significantly speeds up Magnetic Resonance Image
(MRI) processing and achieves accurate MRI reconstruction from under-sampled
k-space data. According to the current research, there are still several
problems with dynamic MRI k-space reconstruction based on CS. 1) There are
differences between the Fourier domain and the Image domain, and the
differences between MRI processing of different domains need to be considered.
2) As three-dimensional data, dynamic MRI has its spatial-temporal
characteristics, which need to calculate the difference and consistency of
surface textures while preserving structural integrity and uniqueness. 3)
Dynamic MRI reconstruction is time-consuming and computationally
resource-dependent. In this paper, we propose a novel robust low-rank dynamic
MRI reconstruction optimization model via highly under-sampled and Discrete
Fourier Transform (DFT) called the Robust Depth Linear Error Decomposition
Model (RDLEDM). Our method mainly includes linear decomposition, double Total
Variation (TV), and double Nuclear Norm (NN) regularizations. By adding linear
image domain error analysis, the noise is reduced after under-sampled and DFT
processing, and the anti-interference ability of the algorithm is enhanced.
Double TV and NN regularizations can utilize both spatial-temporal
characteristics and explore the complementary relationship between different
dimensions in dynamic MRI sequences. In addition, Due to the non-smoothness and
non-convexity of TV and NN terms, it is difficult to optimize the unified
objective model. To address this issue, we utilize a fast algorithm by solving
a primal-dual form of the original problem. Compared with five state-of-the-art
methods, extensive experiments on dynamic MRI data demonstrate the superior
performance of the proposed method in terms of both reconstruction accuracy and
time complexity. | [
"Junpeng Tan",
"Chunmei Qing",
"Xiangmin Xu"
] | 2023-10-23 13:34:59 | http://arxiv.org/abs/2310.14934v1 | http://arxiv.org/pdf/2310.14934v1 | 2310.14934v1 |
Linking Surface Facts to Large-Scale Knowledge Graphs | Open Information Extraction (OIE) methods extract facts from natural language
text in the form of ("subject"; "relation"; "object") triples. These facts are,
however, merely surface forms, the ambiguity of which impedes their downstream
usage; e.g., the surface phrase "Michael Jordan" may refer to either the former
basketball player or the university professor. Knowledge Graphs (KGs), on the
other hand, contain facts in a canonical (i.e., unambiguous) form, but their
coverage is limited by a static schema (i.e., a fixed set of entities and
predicates). To bridge this gap, we need the best of both worlds: (i) high
coverage of free-text OIEs, and (ii) semantic precision (i.e., monosemy) of
KGs. In order to achieve this goal, we propose a new benchmark with novel
evaluation protocols that can, for example, measure fact linking performance on
a granular triple slot level, while also measuring if a system has the ability
to recognize that a surface form has no match in the existing KG. Our extensive
evaluation of several baselines show that detection of out-of-KG entities and
predicates is more difficult than accurate linking to existing ones, thus
calling for more research efforts on this difficult task. We publicly release
all resources (data, benchmark and code) on
https://github.com/nec-research/fact-linking. | [
"Gorjan Radevski",
"Kiril Gashteovski",
"Chia-Chien Hung",
"Carolin Lawrence",
"Goran Glavaš"
] | 2023-10-23 13:18:49 | http://arxiv.org/abs/2310.14909v1 | http://arxiv.org/pdf/2310.14909v1 | 2310.14909v1 |
Series of Hessian-Vector Products for Tractable Saddle-Free Newton Optimisation of Neural Networks | Despite their popularity in the field of continuous optimisation,
second-order quasi-Newton methods are challenging to apply in machine learning,
as the Hessian matrix is intractably large. This computational burden is
exacerbated by the need to address non-convexity, for instance by modifying the
Hessian's eigenvalues as in Saddle-Free Newton methods. We propose an
optimisation algorithm which addresses both of these concerns - to our
knowledge, the first efficiently-scalable optimisation algorithm to
asymptotically use the exact (eigenvalue-modified) inverse Hessian. Our method
frames the problem as a series which principally square-roots and inverts the
squared Hessian, then uses it to precondition a gradient vector, all without
explicitly computing or eigendecomposing the Hessian. A truncation of this
infinite series provides a new optimisation algorithm which is scalable and
comparable to other first- and second-order optimisation methods in both
runtime and optimisation performance. We demonstrate this in a variety of
settings, including a ResNet-18 trained on CIFAR-10. | [
"Elre T. Oldewage",
"Ross M. Clarke",
"José Miguel Hernández-Lobato"
] | 2023-10-23 13:11:30 | http://arxiv.org/abs/2310.14901v1 | http://arxiv.org/pdf/2310.14901v1 | 2310.14901v1 |
Local Universal Rule-based Explanations | Explainable artificial intelligence (XAI) is one of the most intensively
developed are of AI in recent years. It is also one of the most fragmented one
with multiple methods that focus on different aspects of explanations. This
makes difficult to obtain the full spectrum of explanation at once in a compact
and consistent way. To address this issue, we present Local Universal Explainer
(LUX) that is a rule-based explainer which can generate factual, counterfactual
and visual explanations. It is based on a modified version of decision tree
algorithms that allows for oblique splits and integration with feature
importance XAI methods such as SHAP or LIME. It does not use data generation in
opposite to other algorithms, but is focused on selecting local concepts in a
form of high-density clusters of real data that have the highest impact on
forming the decision boundary of the explained model. We tested our method on
real and synthetic datasets and compared it with state-of-the-art rule-based
explainers such as LORE, EXPLAN and Anchor. Our method outperforms currently
existing approaches in terms of simplicity, global fidelity and
representativeness. | [
"Szymon Bobek",
"Grzegorz J. Nalepa"
] | 2023-10-23 13:04:15 | http://arxiv.org/abs/2310.14894v1 | http://arxiv.org/pdf/2310.14894v1 | 2310.14894v1 |
Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support | The posterior in probabilistic programs with stochastic support decomposes as
a weighted sum of the local posterior distributions associated with each
possible program path. We show that making predictions with this full posterior
implicitly performs a Bayesian model averaging (BMA) over paths. This is
potentially problematic, as model misspecification can cause the BMA weights to
prematurely collapse onto a single path, leading to sub-optimal predictions in
turn. To remedy this issue, we propose alternative mechanisms for path
weighting: one based on stacking and one based on ideas from PAC-Bayes. We show
how both can be implemented as a cheap post-processing step on top of existing
inference engines. In our experiments, we find them to be more robust and lead
to better predictions compared to the default BMA weights. | [
"Tim Reichelt",
"Luke Ong",
"Tom Rainforth"
] | 2023-10-23 12:57:03 | http://arxiv.org/abs/2310.14888v1 | http://arxiv.org/pdf/2310.14888v1 | 2310.14888v1 |
A Study on Knowledge Graph Embeddings and Graph Neural Networks for Web Of Things | Graph data structures are widely used to store relational information between
several entities. With data being generated worldwide on a large scale, we see
a significant growth in the generation of knowledge graphs. Thing in the future
is Orange's take on a knowledge graph in the domain of the Web Of Things (WoT),
where the main objective of the platform is to provide a digital representation
of the physical world and enable cross-domain applications to be built upon
this massive and highly connected graph of things. In this context, as the
knowledge graph grows in size, it is prone to have noisy and messy data. In
this paper, we explore state-of-the-art knowledge graph embedding (KGE) methods
to learn numerical representations of the graph entities and, subsequently,
explore downstream tasks like link prediction, node classification, and triple
classification. We also investigate Graph neural networks (GNN) alongside KGEs
and compare their performance on the same downstream tasks. Our evaluation
highlights the encouraging performance of both KGE and GNN-based methods on
node classification, and the superiority of GNN approaches in the link
prediction task. Overall, we show that state-of-the-art approaches are relevant
in a WoT context, and this preliminary work provides insights to implement and
evaluate them in this context. | [
"Rohith Teja Mittakola",
"Thomas Hassan"
] | 2023-10-23 12:36:33 | http://arxiv.org/abs/2310.14866v1 | http://arxiv.org/pdf/2310.14866v1 | 2310.14866v1 |
Diverse Priors for Deep Reinforcement Learning | In Reinforcement Learning (RL), agents aim at maximizing cumulative rewards
in a given environment. During the learning process, RL agents face the dilemma
of exploitation and exploration: leveraging existing knowledge to acquire
rewards or seeking potentially higher ones. Using uncertainty as a guiding
principle provides an active and effective approach to solving this dilemma and
ensemble-based methods are one of the prominent avenues for quantifying
uncertainty. Nevertheless, conventional ensemble-based uncertainty estimation
lacks an explicit prior, deviating from Bayesian principles. Besides, this
method requires diversity among members to generate less biased uncertainty
estimation results. To address the above problems, previous research has
incorporated random functions as priors. Building upon these foundational
efforts, our work introduces an innovative approach with delicately designed
prior NNs, which can incorporate maximal diversity in the initial value
functions of RL. Our method has demonstrated superior performance compared with
the random prior approaches in solving classic control problems and general
exploration tasks, significantly improving sample efficiency. | [
"Chenfan Weng",
"Zhongguo Li"
] | 2023-10-23 12:33:59 | http://arxiv.org/abs/2310.14864v1 | http://arxiv.org/pdf/2310.14864v1 | 2310.14864v1 |
Dynamically Weighted Federated k-Means | Federated clustering is an important part of the field of federated machine
learning, that allows multiple data sources to collaboratively cluster their
data while keeping it decentralized and preserving privacy. In this paper, we
introduce a novel federated clustering algorithm, named Dynamically Weighted
Federated k-means (DWF k-means), to address the challenges posed by distributed
data sources and heterogeneous data. Our proposed algorithm combines the
benefits of traditional clustering techniques with the privacy and scalability
advantages of federated learning. It enables multiple data owners to
collaboratively cluster their local data while exchanging minimal information
with a central coordinator. The algorithm optimizes the clustering process by
adaptively aggregating cluster assignments and centroids from each data source,
thereby learning a global clustering solution that reflects the collective
knowledge of the entire federated network. We conduct experiments on multiple
datasets and data distribution settings to evaluate the performance of our
algorithm in terms of clustering score, accuracy, and v-measure. The results
demonstrate that our approach can match the performance of the centralized
classical k-means baseline, and outperform existing federated clustering
methods in realistic scenarios. | [
"Patrick Holzer",
"Tania Jacob",
"Shubham Kavane"
] | 2023-10-23 12:28:21 | http://arxiv.org/abs/2310.14858v1 | http://arxiv.org/pdf/2310.14858v1 | 2310.14858v1 |
Zero-knowledge Proof Meets Machine Learning in Verifiability: A Survey | With the rapid advancement of artificial intelligence technology, the usage
of machine learning models is gradually becoming part of our daily lives.
High-quality models rely not only on efficient optimization algorithms but also
on the training and learning processes built upon vast amounts of data and
computational power. However, in practice, due to various challenges such as
limited computational resources and data privacy concerns, users in need of
models often cannot train machine learning models locally. This has led them to
explore alternative approaches such as outsourced learning and federated
learning. While these methods address the feasibility of model training
effectively, they introduce concerns about the trustworthiness of the training
process since computations are not performed locally. Similarly, there are
trustworthiness issues associated with outsourced model inference. These two
problems can be summarized as the trustworthiness problem of model
computations: How can one verify that the results computed by other
participants are derived according to the specified algorithm, model, and input
data? To address this challenge, verifiable machine learning (VML) has emerged.
This paper presents a comprehensive survey of zero-knowledge proof-based
verifiable machine learning (ZKP-VML) technology. We first analyze the
potential verifiability issues that may exist in different machine learning
scenarios. Subsequently, we provide a formal definition of ZKP-VML. We then
conduct a detailed analysis and classification of existing works based on their
technical approaches. Finally, we discuss the key challenges and future
directions in the field of ZKP-based VML. | [
"Zhibo Xing",
"Zijian Zhang",
"Jiamou Liu",
"Ziang Zhang",
"Meng Li",
"Liehuang Zhu",
"Giovanni Russello"
] | 2023-10-23 12:15:23 | http://arxiv.org/abs/2310.14848v1 | http://arxiv.org/pdf/2310.14848v1 | 2310.14848v1 |
ULTRA-DP: Unifying Graph Pre-training with Multi-task Graph Dual Prompt | Recent research has demonstrated the efficacy of pre-training graph neural
networks (GNNs) to capture the transferable graph semantics and enhance the
performance of various downstream tasks. However, the semantic knowledge
learned from pretext tasks might be unrelated to the downstream task, leading
to a semantic gap that limits the application of graph pre-training. To reduce
this gap, traditional approaches propose hybrid pre-training to combine various
pretext tasks together in a multi-task learning fashion and learn multi-grained
knowledge, which, however, cannot distinguish tasks and results in some
transferable task-specific knowledge distortion by each other. Moreover, most
GNNs cannot distinguish nodes located in different parts of the graph, making
them fail to learn position-specific knowledge and lead to suboptimal
performance. In this work, inspired by the prompt-based tuning in natural
language processing, we propose a unified framework for graph hybrid
pre-training which injects the task identification and position identification
into GNNs through a prompt mechanism, namely multi-task graph dual prompt
(ULTRA-DP). Based on this framework, we propose a prompt-based transferability
test to find the most relevant pretext task in order to reduce the semantic
gap. To implement the hybrid pre-training tasks, beyond the classical edge
prediction task (node-node level), we further propose a novel pre-training
paradigm based on a group of $k$-nearest neighbors (node-group level). The
combination of them across different scales is able to comprehensively express
more structural semantics and derive richer multi-grained knowledge. Extensive
experiments show that our proposed ULTRA-DP can significantly enhance the
performance of hybrid pre-training methods and show the generalizability to
other pre-training tasks and backbone architectures. | [
"Mouxiang Chen",
"Zemin Liu",
"Chenghao Liu",
"Jundong Li",
"Qiheng Mao",
"Jianling Sun"
] | 2023-10-23 12:11:13 | http://arxiv.org/abs/2310.14845v1 | http://arxiv.org/pdf/2310.14845v1 | 2310.14845v1 |
Calibration of Time-Series Forecasting Transformers: Detecting and Adapting Context-Driven Distribution Shift | Recent years have witnessed the success of introducing Transformers to time
series forecasting. From a data generation perspective, we illustrate that
existing Transformers are susceptible to distribution shifts driven by temporal
contexts, whether observed or unobserved. Such context-driven distribution
shift (CDS) introduces biases in predictions within specific contexts and poses
challenges for conventional training paradigm. In this paper, we introduce a
universal calibration methodology for the detection and adaptation of CDS with
a trained Transformer model. To this end, we propose a novel CDS detector,
termed the "residual-based CDS detector" or "Reconditionor", which quantifies
the model's vulnerability to CDS by evaluating the mutual information between
prediction residuals and their corresponding contexts. A high Reconditionor
score indicates a severe susceptibility, thereby necessitating model
adaptation. In this circumstance, we put forth a straightforward yet potent
adapter framework for model calibration, termed the "sample-level
contextualized adapter" or "SOLID". This framework involves the curation of a
contextually similar dataset to the provided test sample and the subsequent
fine-tuning of the model's prediction layer with a limited number of steps. Our
theoretical analysis demonstrates that this adaptation strategy is able to
achieve an optimal equilibrium between bias and variance. Notably, our proposed
Reconditionor and SOLID are model-agnostic and readily adaptable to a wide
range of Transformers. Extensive experiments show that SOLID consistently
enhances the performance of current SOTA Transformers on real-world datasets,
especially on cases with substantial CDS detected by the proposed
Reconditionor, thus validate the effectiveness of the calibration approach. | [
"Mouxiang Chen",
"Lefei Shen",
"Han Fu",
"Zhuo Li",
"Jianling Sun",
"Chenghao Liu"
] | 2023-10-23 11:58:01 | http://arxiv.org/abs/2310.14838v1 | http://arxiv.org/pdf/2310.14838v1 | 2310.14838v1 |
Harnessing Attention Mechanisms: Efficient Sequence Reduction using Attention-based Autoencoders | Many machine learning models use the manipulation of dimensions as a driving
force to enable models to identify and learn important features in data. In the
case of sequential data this manipulation usually happens on the token
dimension level. Despite the fact that many tasks require a change in sequence
length itself, the step of sequence length reduction usually happens out of
necessity and in a single step. As far as we are aware, no model uses the
sequence length reduction step as an additional opportunity to tune the models
performance. In fact, sequence length manipulation as a whole seems to be an
overlooked direction. In this study we introduce a novel attention-based method
that allows for the direct manipulation of sequence lengths. To explore the
method's capabilities, we employ it in an autoencoder model. The autoencoder
reduces the input sequence to a smaller sequence in latent space. It then aims
to reproduce the original sequence from this reduced form. In this setting, we
explore the methods reduction performance for different input and latent
sequence lengths. We are able to show that the autoencoder retains all the
significant information when reducing the original sequence to half its
original size. When reducing down to as low as a quarter of its original size,
the autoencoder is still able to reproduce the original sequence with an
accuracy of around 90%. | [
"Daniel Biermann",
"Fabrizio Palumbo",
"Morten Goodwin",
"Ole-Christoffer Granmo"
] | 2023-10-23 11:57:44 | http://arxiv.org/abs/2310.14837v1 | http://arxiv.org/pdf/2310.14837v1 | 2310.14837v1 |
Sharp error bounds for imbalanced classification: how many examples in the minority class? | When dealing with imbalanced classification data, reweighting the loss
function is a standard procedure allowing to equilibrate between the true
positive and true negative rates within the risk measure. Despite significant
theoretical work in this area, existing results do not adequately address a
main challenge within the imbalanced classification framework, which is the
negligible size of one class in relation to the full sample size and the need
to rescale the risk function by a probability tending to zero. To address this
gap, we present two novel contributions in the setting where the rare class
probability approaches zero: (1) a non asymptotic fast rate probability bound
for constrained balanced empirical risk minimization, and (2) a consistent
upper bound for balanced nearest neighbors estimates. Our findings provide a
clearer understanding of the benefits of class-weighting in realistic settings,
opening new avenues for further research in this field. | [
"Anass Aghbalou",
"François Portier",
"Anne Sabourin"
] | 2023-10-23 11:45:34 | http://arxiv.org/abs/2310.14826v1 | http://arxiv.org/pdf/2310.14826v1 | 2310.14826v1 |
Text2Topic: Multi-Label Text Classification System for Efficient Topic Detection in User Generated Content with Zero-Shot Capabilities | Multi-label text classification is a critical task in the industry. It helps
to extract structured information from large amount of textual data. We propose
Text to Topic (Text2Topic), which achieves high multi-label classification
performance by employing a Bi-Encoder Transformer architecture that utilizes
concatenation, subtraction, and multiplication of embeddings on both text and
topic. Text2Topic also supports zero-shot predictions, produces domain-specific
text embeddings, and enables production-scale batch-inference with high
throughput. The final model achieves accurate and comprehensive results
compared to state-of-the-art baselines, including large language models (LLMs).
In this study, a total of 239 topics are defined, and around 1.6 million
text-topic pairs annotations (in which 200K are positive) are collected on
approximately 120K texts from 3 main data sources on Booking.com. The data is
collected with optimized smart sampling and partial labeling. The final
Text2Topic model is deployed on a real-world stream processing platform, and it
outperforms other models with 92.9% micro mAP, as well as a 75.8% macro mAP
score. We summarize the modeling choices which are extensively tested through
ablation studies, and share detailed in-production decision-making steps. | [
"Fengjun Wang",
"Moran Beladev",
"Ofri Kleinfeld",
"Elina Frayerman",
"Tal Shachar",
"Eran Fainman",
"Karen Lastmann Assaraf",
"Sarai Mizrachi",
"Benjamin Wang"
] | 2023-10-23 11:33:24 | http://arxiv.org/abs/2310.14817v1 | http://arxiv.org/pdf/2310.14817v1 | 2310.14817v1 |
Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias | Self-training is a well-known approach for semi-supervised learning. It
consists of iteratively assigning pseudo-labels to unlabeled data for which the
model is confident and treating them as labeled examples. For neural networks,
softmax prediction probabilities are often used as a confidence measure,
despite the fact that they are known to be overconfident, even for wrong
predictions. This phenomenon is particularly intensified in the presence of
sample selection bias, i.e., when data labeling is subject to some constraint.
To address this issue, we propose a novel confidence measure, called
$\mathcal{T}$-similarity, built upon the prediction diversity of an ensemble of
linear classifiers. We provide the theoretical analysis of our approach by
studying stationary points and describing the relationship between the
diversity of the individual members and their performance. We empirically
demonstrate the benefit of our confidence measure for three different
pseudo-labeling policies on classification datasets of various data modalities. | [
"Ambroise Odonnat",
"Vasilii Feofanov",
"Ievgen Redko"
] | 2023-10-23 11:30:06 | http://arxiv.org/abs/2310.14814v1 | http://arxiv.org/pdf/2310.14814v1 | 2310.14814v1 |
Learning spatio-temporal patterns with Neural Cellular Automata | Neural Cellular Automata (NCA) are a powerful combination of machine learning
and mechanistic modelling. We train NCA to learn complex dynamics from time
series of images and PDE trajectories. Our method is designed to identify
underlying local rules that govern large scale dynamic emergent behaviours.
Previous work on NCA focuses on learning rules that give stationary emergent
structures. We extend NCA to capture both transient and stable structures
within the same system, as well as learning rules that capture the dynamics of
Turing pattern formation in nonlinear Partial Differential Equations (PDEs). We
demonstrate that NCA can generalise very well beyond their PDE training data,
we show how to constrain NCA to respect given symmetries, and we explore the
effects of associated hyperparameters on model performance and stability. Being
able to learn arbitrary dynamics gives NCA great potential as a data driven
modelling framework, especially for modelling biological pattern formation. | [
"Alex D. Richardson",
"Tibor Antal",
"Richard A. Blythe",
"Linus J. Schumacher"
] | 2023-10-23 11:16:32 | http://arxiv.org/abs/2310.14809v1 | http://arxiv.org/pdf/2310.14809v1 | 2310.14809v1 |
What do Deck Chairs and Sun Hats Have in Common? Uncovering Shared Properties in Large Concept Vocabularies | Concepts play a central role in many applications. This includes settings
where concepts have to be modelled in the absence of sentence context. Previous
work has therefore focused on distilling decontextualised concept embeddings
from language models. But concepts can be modelled from different perspectives,
whereas concept embeddings typically mostly capture taxonomic structure. To
address this issue, we propose a strategy for identifying what different
concepts, from a potentially large concept vocabulary, have in common with
others. We then represent concepts in terms of the properties they share with
the other concepts. To demonstrate the practical usefulness of this way of
modelling concepts, we consider the task of ultra-fine entity typing, which is
a challenging multi-label classification problem. We show that by augmenting
the label set with shared properties, we can improve the performance of the
state-of-the-art models for this task. | [
"Amit Gajbhiye",
"Zied Bouraoui",
"Na Li",
"Usashi Chatterjee",
"Luis Espinosa Anke",
"Steven Schockaert"
] | 2023-10-23 10:53:25 | http://arxiv.org/abs/2310.14793v1 | http://arxiv.org/pdf/2310.14793v1 | 2310.14793v1 |
An Efficient Imbalance-Aware Federated Learning Approach for Wearable Healthcare with Autoregressive Ratio Observation | Widely available healthcare services are now getting popular because of
advancements in wearable sensing techniques and mobile edge computing. People's
health information is collected by edge devices such as smartphones and
wearable bands for further analysis on servers, then send back suggestions and
alerts for abnormal conditions. The recent emergence of federated learning
allows users to train private data on local devices while updating models
collaboratively. However, the heterogeneous distribution of the health
condition data may lead to significant risks to model performance due to class
imbalance. Meanwhile, as FL training is powered by sharing gradients only with
the server, training data is almost inaccessible. The conventional solutions to
class imbalance do not work for federated learning. In this work, we propose a
new federated learning framework FedImT, dedicated to addressing the challenges
of class imbalance in federated learning scenarios. FedImT contains an online
scheme that can estimate the data composition during each round of aggregation,
then introduces a self-attenuating iterative equivalent to track variations of
multiple estimations and promptly tweak the balance of the loss computing for
minority classes. Experiments demonstrate the effectiveness of FedImT in
solving the imbalance problem without extra energy consumption and avoiding
privacy risks. | [
"Wenhao Yan",
"He Li",
"Kaoru Ota",
"Mianxiong Dong"
] | 2023-10-23 10:36:52 | http://arxiv.org/abs/2310.14784v1 | http://arxiv.org/pdf/2310.14784v1 | 2310.14784v1 |
Geographical Erasure in Language Generation | Large language models (LLMs) encode vast amounts of world knowledge. However,
since these models are trained on large swaths of internet data, they are at
risk of inordinately capturing information about dominant groups. This
imbalance can propagate into generated language. In this work, we study and
operationalise a form of geographical erasure, wherein language models
underpredict certain countries. We demonstrate consistent instances of erasure
across a range of LLMs. We discover that erasure strongly correlates with low
frequencies of country mentions in the training corpus. Lastly, we mitigate
erasure by finetuning using a custom objective. | [
"Pola Schwöbel",
"Jacek Golebiowski",
"Michele Donini",
"Cédric Archambeau",
"Danish Pruthi"
] | 2023-10-23 10:26:14 | http://arxiv.org/abs/2310.14777v1 | http://arxiv.org/pdf/2310.14777v1 | 2310.14777v1 |
Principled Approaches for Learning to Defer with Multiple Experts | We present a study of surrogate losses and algorithms for the general problem
of learning to defer with multiple experts. We first introduce a new family of
surrogate losses specifically tailored for the multiple-expert setting, where
the prediction and deferral functions are learned simultaneously. We then prove
that these surrogate losses benefit from strong $H$-consistency bounds. We
illustrate the application of our analysis through several examples of
practical surrogate losses, for which we give explicit guarantees. These loss
functions readily lead to the design of new learning to defer algorithms based
on their minimization. While the main focus of this work is a theoretical
analysis, we also report the results of several experiments on SVHN and
CIFAR-10 datasets. | [
"Anqi Mao",
"Mehryar Mohri",
"Yutao Zhong"
] | 2023-10-23 10:19:09 | http://arxiv.org/abs/2310.14774v1 | http://arxiv.org/pdf/2310.14774v1 | 2310.14774v1 |
Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms | We study the key framework of learning with abstention in the multi-class
classification setting. In this setting, the learner can choose to abstain from
making a prediction with some pre-defined cost. We present a series of new
theoretical and algorithmic results for this learning problem in the
predictor-rejector framework. We introduce several new families of surrogate
losses for which we prove strong non-asymptotic and hypothesis set-specific
consistency guarantees, thereby resolving positively two existing open
questions. These guarantees provide upper bounds on the estimation error of the
abstention loss function in terms of that of the surrogate loss. We analyze
both a single-stage setting where the predictor and rejector are learned
simultaneously and a two-stage setting crucial in applications, where the
predictor is learned in a first stage using a standard surrogate loss such as
cross-entropy. These guarantees suggest new multi-class abstention algorithms
based on minimizing these surrogate losses. We also report the results of
extensive experiments comparing these algorithms to the current
state-of-the-art algorithms on CIFAR-10, CIFAR-100 and SVHN datasets. Our
results demonstrate empirically the benefit of our new surrogate losses and
show the remarkable performance of our broadly applicable two-stage abstention
algorithm. | [
"Anqi Mao",
"Mehryar Mohri",
"Yutao Zhong"
] | 2023-10-23 10:16:27 | http://arxiv.org/abs/2310.14772v1 | http://arxiv.org/pdf/2310.14772v1 | 2310.14772v1 |
Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstention | Learning with abstention is a key scenario where the learner can abstain from
making a prediction at some cost. In this paper, we analyze the score-based
formulation of learning with abstention in the multi-class classification
setting. We introduce new families of surrogate losses for the abstention loss
function, which include the state-of-the-art surrogate losses in the
single-stage setting and a novel family of loss functions in the two-stage
setting. We prove strong non-asymptotic and hypothesis set-specific consistency
guarantees for these surrogate losses, which upper-bound the estimation error
of the abstention loss function in terms of the estimation error of the
surrogate loss. Our bounds can help compare different score-based surrogates
and guide the design of novel abstention algorithms by minimizing the proposed
surrogate losses. We experimentally evaluate our new algorithms on CIFAR-10,
CIFAR-100, and SVHN datasets and the practical significance of our new
surrogate losses and two-stage abstention algorithms. Our results also show
that the relative performance of the state-of-the-art score-based surrogate
losses can vary across datasets. | [
"Anqi Mao",
"Mehryar Mohri",
"Yutao Zhong"
] | 2023-10-23 10:13:35 | http://arxiv.org/abs/2310.14770v1 | http://arxiv.org/pdf/2310.14770v1 | 2310.14770v1 |
Policy Gradient with Kernel Quadrature | Reward evaluation of episodes becomes a bottleneck in a broad range of
reinforcement learning tasks. Our aim in this paper is to select a small but
representative subset of a large batch of episodes, only on which we actually
compute rewards for more efficient policy gradient iterations. We build a
Gaussian process modeling of discounted returns or rewards to derive a positive
definite kernel on the space of episodes, run an "episodic" kernel quadrature
method to compress the information of sample episodes, and pass the reduced
episodes to the policy network for gradient updates. We present the theoretical
background of this procedure as well as its numerical illustrations in MuJoCo
and causal discovery tasks. | [
"Satoshi Hayakawa",
"Tetsuro Morimura"
] | 2023-10-23 10:12:23 | http://arxiv.org/abs/2310.14768v1 | http://arxiv.org/pdf/2310.14768v1 | 2310.14768v1 |
Improved K-mer Based Prediction of Protein-Protein Interactions With Chaos Game Representation, Deep Learning and Reduced Representation Bias | Protein-protein interactions drive many biological processes, including the
detection of phytopathogens by plants' R-Proteins and cell surface receptors.
Many machine learning studies have attempted to predict protein-protein
interactions but performance is highly dependent on training data; models have
been shown to accurately predict interactions when the proteins involved are
included in the training data, but achieve consistently poorer results when
applied to previously unseen proteins. In addition, models that are trained
using proteins that take part in multiple interactions can suffer from
representation bias, where predictions are driven not by learned biological
features but by learning of the structure of the interaction dataset.
We present a method for extracting unique pairs from an interaction dataset,
generating non-redundant paired data for unbiased machine learning. After
applying the method to datasets containing _Arabidopsis thaliana_ and pathogen
effector interations, we developed a convolutional neural network model capable
of learning and predicting interactions from Chaos Game Representations of
proteins' coding genes. | [
"Ruth Veevers",
"Dan MacLean"
] | 2023-10-23 10:02:23 | http://arxiv.org/abs/2310.14764v1 | http://arxiv.org/pdf/2310.14764v1 | 2310.14764v1 |
Externally Valid Policy Evaluation Combining Trial and Observational Data | Randomized trials are widely considered as the gold standard for evaluating
the effects of decision policies. Trial data is, however, drawn from a
population which may differ from the intended target population and this raises
a problem of external validity (aka. generalizability). In this paper we seek
to use trial data to draw valid inferences about the outcome of a policy on the
target population. Additional covariate data from the target population is used
to model the sampling of individuals in the trial study. We develop a method
that yields certifiably valid trial-based policy evaluations under any
specified range of model miscalibrations. The method is nonparametric and the
validity is assured even with finite samples. The certified policy evaluations
are illustrated using both simulated and real data. | [
"Sofia Ek",
"Dave Zachariah"
] | 2023-10-23 10:01:50 | http://arxiv.org/abs/2310.14763v1 | http://arxiv.org/pdf/2310.14763v1 | 2310.14763v1 |
Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules | Masked graph modeling excels in the self-supervised representation learning
of molecular graphs. Scrutinizing previous studies, we can reveal a common
scheme consisting of three key components: (1) graph tokenizer, which breaks a
molecular graph into smaller fragments (i.e., subgraphs) and converts them into
tokens; (2) graph masking, which corrupts the graph with masks; (3) graph
autoencoder, which first applies an encoder on the masked graph to generate the
representations, and then employs a decoder on the representations to recover
the tokens of the original graph. However, the previous MGM studies focus
extensively on graph masking and encoder, while there is limited understanding
of tokenizer and decoder. To bridge the gap, we first summarize popular
molecule tokenizers at the granularity of node, edge, motif, and Graph Neural
Networks (GNNs), and then examine their roles as the MGM's reconstruction
targets. Further, we explore the potential of adopting an expressive decoder in
MGM. Our results show that a subgraph-level tokenizer and a sufficiently
expressive decoder with remask decoding have a large impact on the encoder's
representation learning. Finally, we propose a novel MGM method SimSGT,
featuring a Simple GNN-based Tokenizer (SGT) and an effective decoding
strategy. We empirically validate that our method outperforms the existing
molecule self-supervised learning methods. Our codes and checkpoints are
available at https://github.com/syr-cn/SimSGT. | [
"Zhiyuan Liu",
"Yaorui Shi",
"An Zhang",
"Enzhi Zhang",
"Kenji Kawaguchi",
"Xiang Wang",
"Tat-Seng Chua"
] | 2023-10-23 09:40:30 | http://arxiv.org/abs/2310.14753v1 | http://arxiv.org/pdf/2310.14753v1 | 2310.14753v1 |
Efficient and Interpretable Bandit Algorithms | Motivated by the importance of explainability in modern machine learning, we
design bandit algorithms that are \emph{efficient} and \emph{interpretable}. A
bandit algorithm is interpretable if it explores with the objective of reducing
uncertainty in the unknown model parameter. To quantify the interpretability,
we introduce a novel metric of \textit{uncertainty loss}, which compares the
rate of the uncertainty reduction to the theoretical optimum. We propose CODE,
a bandit algorithm based on a \textbf{C}onstrained \textbf{O}ptimal
\textbf{DE}sign, that is interpretable and maximally reduces the uncertainty.
The key idea in \code is to explore among all plausible actions, determined by
a statistical constraint, to achieve interpretability. We implement CODE
efficiently in both multi-armed and linear bandits and derive near-optimal
regret bounds by leveraging the optimality criteria of the approximate optimal
design. CODE can be also viewed as removing phases in conventional phased
elimination, which makes it more practical and general. We demonstrate the
advantage of \code by numerical experiments on both synthetic and real-world
problems. CODE outperforms other state-of-the-art interpretable designs while
matching the performance of popular but uninterpretable designs, such as upper
confidence bound algorithms. | [
"Subhojyoti Mukherjee",
"Ruihao Zhu",
"Branislav Kveton"
] | 2023-10-23 09:36:13 | http://arxiv.org/abs/2310.14751v1 | http://arxiv.org/pdf/2310.14751v1 | 2310.14751v1 |
The Safety Challenges of Deep Learning in Real-World Type 1 Diabetes Management | Blood glucose simulation allows the effectiveness of type 1 diabetes (T1D)
management strategies to be evaluated without patient harm. Deep learning
algorithms provide a promising avenue for extending simulator capabilities;
however, these algorithms are limited in that they do not necessarily learn
physiologically correct glucose dynamics and can learn incorrect and
potentially dangerous relationships from confounders in training data. This is
likely to be more important in real-world scenarios, as data is not collected
under strict research protocol. This work explores the implications of using
deep learning algorithms trained on real-world data to model glucose dynamics.
Free-living data was processed from the OpenAPS Data Commons and supplemented
with patient-reported tags of challenging diabetes events, constituting one of
the most detailed real-world T1D datasets. This dataset was used to train and
evaluate state-of-the-art glucose simulators, comparing their prediction error
across safety critical scenarios and assessing the physiological
appropriateness of the learned dynamics using Shapley Additive Explanations
(SHAP). While deep learning prediction accuracy surpassed the widely-used
mathematical simulator approach, the model deteriorated in safety critical
scenarios and struggled to leverage self-reported meal and exercise
information. SHAP value analysis also indicated the model had fundamentally
confused the roles of insulin and carbohydrates, which is one of the most basic
T1D management principles. This work highlights the importance of considering
physiological appropriateness when using deep learning to model real-world
systems in T1D and healthcare more broadly, and provides recommendations for
building models that are robust to real-world data constraints. | [
"Harry Emerson",
"Ryan McConville",
"Matthew Guy"
] | 2023-10-23 09:25:50 | http://arxiv.org/abs/2310.14743v1 | http://arxiv.org/pdf/2310.14743v1 | 2310.14743v1 |
Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks | Data preprocessing is a crucial part of any machine learning pipeline, and it
can have a significant impact on both performance and training efficiency. This
is especially evident when using deep neural networks for time series
prediction and classification: real-world time series data often exhibit
irregularities such as multi-modality, skewness and outliers, and the model
performance can degrade rapidly if these characteristics are not adequately
addressed. In this work, we propose the EDAIN (Extended Deep Adaptive Input
Normalization) layer, a novel adaptive neural layer that learns how to
appropriately normalize irregular time series data for a given task in an
end-to-end fashion, instead of using a fixed normalization scheme. This is
achieved by optimizing its unknown parameters simultaneously with the deep
neural network using back-propagation. Our experiments, conducted using
synthetic data, a credit default prediction dataset, and a large-scale limit
order book benchmark dataset, demonstrate the superior performance of the EDAIN
layer when compared to conventional normalization methods and existing adaptive
time series preprocessing layers. | [
"Marcus A. K. September",
"Francesco Sanna Passino",
"Leonie Goldmann",
"Anton Hinel"
] | 2023-10-23 08:56:01 | http://arxiv.org/abs/2310.14720v1 | http://arxiv.org/pdf/2310.14720v1 | 2310.14720v1 |
BatteryML:An Open-source platform for Machine Learning on Battery Degradation | Battery degradation remains a pivotal concern in the energy storage domain,
with machine learning emerging as a potent tool to drive forward insights and
solutions. However, this intersection of electrochemical science and machine
learning poses complex challenges. Machine learning experts often grapple with
the intricacies of battery science, while battery researchers face hurdles in
adapting intricate models tailored to specific datasets. Beyond this, a
cohesive standard for battery degradation modeling, inclusive of data formats
and evaluative benchmarks, is conspicuously absent. Recognizing these
impediments, we present BatteryML - a one-step, all-encompass, and open-source
platform designed to unify data preprocessing, feature extraction, and the
implementation of both traditional and state-of-the-art models. This
streamlined approach promises to enhance the practicality and efficiency of
research applications. BatteryML seeks to fill this void, fostering an
environment where experts from diverse specializations can collaboratively
contribute, thus elevating the collective understanding and advancement of
battery research.The code for our project is publicly available on GitHub at
https://github.com/microsoft/BatteryML. | [
"Han Zhang",
"Xiaofan Gui",
"Shun Zheng",
"Ziheng Lu",
"Yuqi Li",
"Jiang Bian"
] | 2023-10-23 08:51:05 | http://arxiv.org/abs/2310.14714v1 | http://arxiv.org/pdf/2310.14714v1 | 2310.14714v1 |
Random Forest Dissimilarity for High-Dimension Low Sample Size Classification | High dimension, low sample size (HDLSS) problems are numerous among
real-world applications of machine learning. From medical images to text
processing, traditional machine learning algorithms are usually unsuccessful in
learning the best possible concept from such data. In a previous work, we
proposed a dissimilarity-based approach for multi-view classification, the
Random Forest Dissimilarity (RFD), that perfoms state-of-the-art results for
such problems. In this work, we transpose the core principle of this approach
to solving HDLSS classification problems, by using the RF similarity measure as
a learned precomputed SVM kernel (RFSVM). We show that such a learned
similarity measure is particularly suited and accurate for this classification
context. Experiments conducted on 40 public HDLSS classification datasets,
supported by rigorous statistical analyses, show that the RFSVM method
outperforms existing methods for the majority of HDLSS problems and remains at
the same time very competitive for low or non-HDLSS problems. | [
"Lucca Portes Cavalheiro",
"Simon Bernard",
"Jean Paul Barddal",
"Laurent Heutte"
] | 2023-10-23 08:49:39 | http://arxiv.org/abs/2310.14710v1 | http://arxiv.org/pdf/2310.14710v1 | 2310.14710v1 |
A Hybrid GNN approach for predicting node data for 3D meshes | Metal forging is used to manufacture dies. We require the best set of input
parameters for the process to be efficient. Currently, we predict the best
parameters using the finite element method by generating simulations for the
different initial conditions, which is a time-consuming process. In this paper,
introduce a hybrid approach that helps in processing and generating new data
simulations using a surrogate graph neural network model based on graph
convolutions, having a cheaper time cost. We also introduce a hybrid approach
that helps in processing and generating new data simulations using the model.
Given a dataset representing meshes, our focus is on the conversion of the
available information into a graph or point cloud structure. This new
representation enables deep learning. The predicted result is similar, with a
low error when compared to that produced using the finite element method. The
new models have outperformed existing PointNet and simple graph neural network
models when applied to produce the simulations. | [
"Shwetha Salimath",
"Francesca Bugiotti",
"Frederic Magoules"
] | 2023-10-23 08:47:27 | http://arxiv.org/abs/2310.14707v1 | http://arxiv.org/pdf/2310.14707v1 | 2310.14707v1 |
Federated learning compression designed for lightweight communications | Federated Learning (FL) is a promising distributed method for edge-level
machine learning, particularly for privacysensitive applications such as those
in military and medical domains, where client data cannot be shared or
transferred to a cloud computing server. In many use-cases, communication cost
is a major challenge in FL due to its natural intensive network usage. Client
devices, such as smartphones or Internet of Things (IoT) nodes, have limited
resources in terms of energy, computation, and memory. To address these
hardware constraints, lightweight models and compression techniques such as
pruning and quantization are commonly adopted in centralised paradigms. In this
paper, we investigate the impact of compression techniques on FL for a typical
image classification task. Going further, we demonstrate that a straightforward
method can compresses messages up to 50% while having less than 1% of accuracy
loss, competing with state-of-the-art techniques. | [
"Lucas Grativol Ribeiro",
"Mathieu Leonardon",
"Guillaume Muller",
"Virginie Fresse",
"Matthieu Arzel"
] | 2023-10-23 08:36:21 | http://arxiv.org/abs/2310.14693v1 | http://arxiv.org/pdf/2310.14693v1 | 2310.14693v1 |
Population Descent: A Natural-Selection Based Hyper-Parameter Tuning Framework | First-order gradient descent has been the base of the most successful
optimization algorithms ever implemented. On supervised learning problems with
very high dimensionality, such as neural network optimization, it is almost
always the algorithm of choice, mainly due to its memory and computational
efficiency. However, it is a classical result in optimization that gradient
descent converges to local minima on non-convex functions. Even more
importantly, in certain high-dimensional cases, escaping the plateaus of large
saddle points becomes intractable. On the other hand, black-box optimization
methods are not sensitive to the local structure of a loss function's landscape
but suffer the curse of dimensionality. Instead, memetic algorithms aim to
combine the benefits of both. Inspired by this, we present Population Descent,
a memetic algorithm focused on hyperparameter optimization. We show that an
adaptive m-elitist selection approach combined with a normalized-fitness-based
randomization scheme outperforms more complex state-of-the-art algorithms by up
to 13% on common benchmark tasks. | [
"Abhinav Pomalapally",
"Bassel El Mabsout",
"Renato Mansuco"
] | 2023-10-23 08:11:17 | http://arxiv.org/abs/2310.14671v1 | http://arxiv.org/pdf/2310.14671v1 | 2310.14671v1 |
Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyond | Vision-language (VL) understanding tasks evaluate models' comprehension of
complex visual scenes through multiple-choice questions. However, we have
identified two dataset biases that models can exploit as shortcuts to resolve
various VL tasks correctly without proper understanding. The first type of
dataset bias is \emph{Unbalanced Matching} bias, where the correct answer
overlaps the question and image more than the incorrect answers. The second
type of dataset bias is \emph{Distractor Similarity} bias, where incorrect
answers are overly dissimilar to the correct answer but significantly similar
to other incorrect answers within the same sample. To address these dataset
biases, we first propose Adversarial Data Synthesis (ADS) to generate synthetic
training and debiased evaluation data. We then introduce Intra-sample
Counterfactual Training (ICT) to assist models in utilizing the synthesized
training data, particularly the counterfactual data, via focusing on
intra-sample differentiation. Extensive experiments demonstrate the
effectiveness of ADS and ICT in consistently improving model performance across
different benchmarks, even in domain-shifted scenarios. | [
"Zhecan Wang",
"Long Chen",
"Haoxuan You",
"Keyang Xu",
"Yicheng He",
"Wenhao Li",
"Noal Codella",
"Kai-Wei Chang",
"Shih-Fu Chang"
] | 2023-10-23 08:09:42 | http://arxiv.org/abs/2310.14670v1 | http://arxiv.org/pdf/2310.14670v1 | 2310.14670v1 |
Data Pruning via Moving-one-Sample-out | In this paper, we propose a novel data-pruning approach called
moving-one-sample-out (MoSo), which aims to identify and remove the least
informative samples from the training set. The core insight behind MoSo is to
determine the importance of each sample by assessing its impact on the optimal
empirical risk. This is achieved by measuring the extent to which the empirical
risk changes when a particular sample is excluded from the training set.
Instead of using the computationally expensive leaving-one-out-retraining
procedure, we propose an efficient first-order approximator that only requires
gradient information from different training stages. The key idea behind our
approximation is that samples with gradients that are consistently aligned with
the average gradient of the training set are more informative and should
receive higher scores, which could be intuitively understood as follows: if the
gradient from a specific sample is consistent with the average gradient vector,
it implies that optimizing the network using the sample will yield a similar
effect on all remaining samples. Experimental results demonstrate that MoSo
effectively mitigates severe performance degradation at high pruning ratios and
achieves satisfactory performance across various settings. | [
"Haoru Tan",
"Sitong Wu",
"Fei Du",
"Yukang Chen",
"Zhibin Wang",
"Fan Wang",
"Xiaojuan Qi"
] | 2023-10-23 08:00:03 | http://arxiv.org/abs/2310.14664v1 | http://arxiv.org/pdf/2310.14664v1 | 2310.14664v1 |
Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy | Posterior sampling, i.e., exponential mechanism to sample from the posterior
distribution, provides $\varepsilon$-pure differential privacy (DP) guarantees
and does not suffer from potentially unbounded privacy breach introduced by
$(\varepsilon,\delta)$-approximate DP. In practice, however, one needs to apply
approximate sampling methods such as Markov chain Monte Carlo (MCMC), thus
re-introducing the unappealing $\delta$-approximation error into the privacy
guarantees. To bridge this gap, we propose the Approximate SAample Perturbation
(abbr. ASAP) algorithm which perturbs an MCMC sample with noise proportional to
its Wasserstein-infinity ($W_\infty$) distance from a reference distribution
that satisfies pure DP or pure Gaussian DP (i.e., $\delta=0$). We then leverage
a Metropolis-Hastings algorithm to generate the sample and prove that the
algorithm converges in W$_\infty$ distance. We show that by combining our new
techniques with a careful localization step, we obtain the first nearly
linear-time algorithm that achieves the optimal rates in the DP-ERM problem
with strongly convex and smooth losses. | [
"Yingyu Lin",
"Yian Ma",
"Yu-Xiang Wang",
"Rachel Redberg"
] | 2023-10-23 07:54:39 | http://arxiv.org/abs/2310.14661v1 | http://arxiv.org/pdf/2310.14661v1 | 2310.14661v1 |
Predicting Accurate Lagrangian Multipliers for Mixed Integer Linear Programs | Lagrangian relaxation stands among the most efficient approaches for solving
a Mixed Integer Linear Programs (MILP) with difficult constraints. Given any
duals for these constraints, called Lagrangian Multipliers (LMs), it returns a
bound on the optimal value of the MILP, and Lagrangian methods seek the LMs
giving the best such bound. But these methods generally rely on iterative
algorithms resembling gradient descent to maximize the concave piecewise linear
dual function: the computational burden grows quickly with the number of
relaxed constraints. We introduce a deep learning approach that bypasses the
descent, effectively amortizing the local, per instance, optimization. A
probabilistic encoder based on a graph convolutional network computes
high-dimensional representations of relaxed constraints in MILP instances. A
decoder then turns these representations into LMs. We train the encoder and
decoder jointly by directly optimizing the bound obtained from the predicted
multipliers. Numerical experiments show that our approach closes up to 85~\% of
the gap between the continuous relaxation and the best Lagrangian bound, and
provides a high quality warm-start for descent based Lagrangian methods. | [
"Francesco Demelas",
"Joseph Le Roux",
"Mathieu Lacroix",
"Axel Parmentier"
] | 2023-10-23 07:53:47 | http://arxiv.org/abs/2310.14659v1 | http://arxiv.org/pdf/2310.14659v1 | 2310.14659v1 |
$Λ$-Split: A Privacy-Preserving Split Computing Framework for Cloud-Powered Generative AI | In the wake of the burgeoning expansion of generative artificial intelligence
(AI) services, the computational demands inherent to these technologies
frequently necessitate cloud-powered computational offloading, particularly for
resource-constrained mobile devices. These services commonly employ prompts to
steer the generative process, and both the prompts and the resultant content,
such as text and images, may harbor privacy-sensitive or confidential
information, thereby elevating security and privacy risks. To mitigate these
concerns, we introduce $\Lambda$-Split, a split computing framework to
facilitate computational offloading while simultaneously fortifying data
privacy against risks such as eavesdropping and unauthorized access. In
$\Lambda$-Split, a generative model, usually a deep neural network (DNN), is
partitioned into three sub-models and distributed across the user's local
device and a cloud server: the input-side and output-side sub-models are
allocated to the local, while the intermediate, computationally-intensive
sub-model resides on the cloud server. This architecture ensures that only the
hidden layer outputs are transmitted, thereby preventing the external
transmission of privacy-sensitive raw input and output data. Given the
black-box nature of DNNs, estimating the original input or output from
intercepted hidden layer outputs poses a significant challenge for malicious
eavesdroppers. Moreover, $\Lambda$-Split is orthogonal to traditional
encryption-based security mechanisms, offering enhanced security when deployed
in conjunction. We empirically validate the efficacy of the $\Lambda$-Split
framework using Llama 2 and Stable Diffusion XL, representative large language
and diffusion models developed by Meta and Stability AI, respectively. Our
$\Lambda$-Split implementation is publicly accessible at
https://github.com/nishio-laboratory/lambda_split. | [
"Shoki Ohta",
"Takayuki Nishio"
] | 2023-10-23 07:44:04 | http://arxiv.org/abs/2310.14651v1 | http://arxiv.org/pdf/2310.14651v1 | 2310.14651v1 |
Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval | Deep hashing has been intensively studied and successfully applied in
large-scale image retrieval systems due to its efficiency and effectiveness.
Recent studies have recognized that the existence of adversarial examples poses
a security threat to deep hashing models, that is, adversarial vulnerability.
Notably, it is challenging to efficiently distill reliable semantic
representatives for deep hashing to guide adversarial learning, and thereby it
hinders the enhancement of adversarial robustness of deep hashing-based
retrieval models. Moreover, current researches on adversarial training for deep
hashing are hard to be formalized into a unified minimax structure. In this
paper, we explore Semantic-Aware Adversarial Training (SAAT) for improving the
adversarial robustness of deep hashing models. Specifically, we conceive a
discriminative mainstay features learning (DMFL) scheme to construct semantic
representatives for guiding adversarial learning in deep hashing. Particularly,
our DMFL with the strict theoretical guarantee is adaptively optimized in a
discriminative learning manner, where both discriminative and semantic
properties are jointly considered. Moreover, adversarial examples are
fabricated by maximizing the Hamming distance between the hash codes of
adversarial samples and mainstay features, the efficacy of which is validated
in the adversarial attack trials. Further, we, for the first time, formulate
the formalized adversarial training of deep hashing into a unified minimax
optimization under the guidance of the generated mainstay codes. Extensive
experiments on benchmark datasets show superb attack performance against the
state-of-the-art algorithms, meanwhile, the proposed adversarial training can
effectively eliminate adversarial perturbations for trustworthy deep
hashing-based retrieval. Our code is available at
https://github.com/xandery-geek/SAAT. | [
"Xu Yuan",
"Zheng Zhang",
"Xunguang Wang",
"Lin Wu"
] | 2023-10-23 07:21:40 | http://arxiv.org/abs/2310.14637v1 | http://arxiv.org/pdf/2310.14637v1 | 2310.14637v1 |
Extending Input Contexts of Language Models through Training on Segmented Sequences | Effectively training language models on long inputs poses many technical
challenges. As a cost consideration, languages models are pretrained on a fixed
sequence length before being adapted to longer sequences. We explore various
methods for adapting models to longer inputs by training on segmented sequences
and an interpolation-based method for extending absolute positional embeddings.
We develop a training procedure to extend the input context size of pretrained
models with no architectural changes and no additional memory costs than
training on the original input lengths. By sub-sampling segments from long
inputs while maintaining their original position the model is able to learn new
positional interactions. Our method benefits both models trained with absolute
positional embeddings, by extending their input contexts, as well as popular
relative positional embedding methods showing a reduced perplexity on sequences
longer than they were trained on. We demonstrate our method can extend input
contexts by a factor of 4x while improving perplexity. | [
"Petros Karypis",
"Julian McAuley",
"George Karypis"
] | 2023-10-23 07:13:31 | http://arxiv.org/abs/2310.14633v1 | http://arxiv.org/pdf/2310.14633v1 | 2310.14633v1 |
Making informed decisions in cutting tool maintenance in milling: A KNN based model agnostic approach | In machining processes, monitoring the condition of the tool is a crucial
aspect to ensure high productivity and quality of the product. Using different
machine learning techniques in Tool Condition Monitoring TCM enables a better
analysis of the large amount of data of different signals acquired during the
machining processes. The real time force signals encountered during the process
were acquired by performing numerous experiments. Different tool wear
conditions were considered during the experimentation. A comprehensive
statistical analysis of the data and feature selection using decision trees was
conducted, and the KNN algorithm was used to perform classification.
Hyperparameter tuning of the model was done to improve the models performance.
Much research has been done to employ machine learning approaches in tool
condition monitoring systems, however, a model agnostic approach to increase
the interpretability of the process and get an in depth understanding of how
the decision making is done is not implemented by many. This research paper
presents a KNN based white box model, which allows us to dive deep into how the
model performs the classification and how it prioritizes the different features
included. This approach helps in detecting why the tool is in a certain
condition and allows the manufacturer to make an informed decision about the
tools maintenance. | [
"Aditya M. Rahalkar",
"Om M. Khare",
"Abhishek D. Patange"
] | 2023-10-23 07:02:30 | http://arxiv.org/abs/2310.14629v1 | http://arxiv.org/pdf/2310.14629v1 | 2310.14629v1 |
CrisisMatch: Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification | The shared real-time information about natural disasters on social media
platforms like Twitter and Facebook plays a critical role in informing
volunteers, emergency managers, and response organizations. However, supervised
learning models for monitoring disaster events require large amounts of
annotated data, making them unrealistic for real-time use in disaster events.
To address this challenge, we present a fine-grained disaster tweet
classification model under the semi-supervised, few-shot learning setting where
only a small number of annotated data is required. Our model, CrisisMatch,
effectively classifies tweets into fine-grained classes of interest using few
labeled data and large amounts of unlabeled data, mimicking the early stage of
a disaster. Through integrating effective semi-supervised learning ideas and
incorporating TextMixUp, CrisisMatch achieves performance improvement on two
disaster datasets of 11.2\% on average. Further analyses are also provided for
the influence of the number of labeled data and out-of-domain results. | [
"Henry Peng Zou",
"Yue Zhou",
"Cornelia Caragea",
"Doina Caragea"
] | 2023-10-23 07:01:09 | http://arxiv.org/abs/2310.14627v1 | http://arxiv.org/pdf/2310.14627v1 | 2310.14627v1 |
CoF-CoT: Enhancing Large Language Models with Coarse-to-Fine Chain-of-Thought Prompting for Multi-domain NLU Tasks | While Chain-of-Thought prompting is popular in reasoning tasks, its
application to Large Language Models (LLMs) in Natural Language Understanding
(NLU) is under-explored. Motivated by multi-step reasoning of LLMs, we propose
Coarse-to-Fine Chain-of-Thought (CoF-CoT) approach that breaks down NLU tasks
into multiple reasoning steps where LLMs can learn to acquire and leverage
essential concepts to solve tasks from different granularities. Moreover, we
propose leveraging semantic-based Abstract Meaning Representation (AMR)
structured knowledge as an intermediate step to capture the nuances and diverse
structures of utterances, and to understand connections between their varying
levels of granularity. Our proposed approach is demonstrated effective in
assisting the LLMs adapt to the multi-grained NLU tasks under both zero-shot
and few-shot multi-domain settings. | [
"Hoang H. Nguyen",
"Ye Liu",
"Chenwei Zhang",
"Tao Zhang",
"Philip S. Yu"
] | 2023-10-23 06:54:51 | http://arxiv.org/abs/2310.14623v1 | http://arxiv.org/pdf/2310.14623v1 | 2310.14623v1 |
Rethinking SIGN Training: Provable Nonconvex Acceleration without First- and Second-Order Gradient Lipschitz | Sign-based stochastic methods have gained attention due to their ability to
achieve robust performance despite using only the sign information for
parameter updates. However, the current convergence analysis of sign-based
methods relies on the strong assumptions of first-order gradient Lipschitz and
second-order gradient Lipschitz, which may not hold in practical tasks like
deep neural network training that involve high non-smoothness. In this paper,
we revisit sign-based methods and analyze their convergence under more
realistic assumptions of first- and second-order smoothness. We first establish
the convergence of the sign-based method under weak first-order Lipschitz.
Motivated by the weak first-order Lipschitz, we propose a relaxed second-order
condition that still allows for nonconvex acceleration in sign-based methods.
Based on our theoretical results, we gain insights into the computational
advantages of the recently developed LION algorithm. In distributed settings,
we prove that this nonconvex acceleration persists with linear speedup in the
number of nodes, when utilizing fast communication compression gossip
protocols. The novelty of our theoretical results lies in that they are derived
under much weaker assumptions, thereby expanding the provable applicability of
sign-based algorithms to a wider range of problems. | [
"Tao Sun",
"Congliang Chen",
"Peng Qiao",
"Li Shen",
"Xinwang Liu",
"Dongsheng Li"
] | 2023-10-23 06:48:43 | http://arxiv.org/abs/2310.14616v1 | http://arxiv.org/pdf/2310.14616v1 | 2310.14616v1 |
CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference | We propose a novel statistical method for testing the results of anomaly
detection (AD) under domain adaptation (DA), which we call CAD-DA --
controllable AD under DA. The distinct advantage of the CAD-DA lies in its
ability to control the probability of misidentifying anomalies under a
pre-specified level $\alpha$ (e.g., 0.05). The challenge within this DA setting
is the necessity to account for the influence of DA to ensure the validity of
the inference results. Our solution to this challenge leverages the concept of
conditional Selective Inference to handle the impact of DA. To our knowledge,
this is the first work capable of conducting a valid statistical inference
within the context of DA. We evaluate the performance of the CAD-DA method on
both synthetic and real-world datasets. | [
"Vo Nguyen Le Duy",
"Hsuan-Tien Lin",
"Ichiro Takeuchi"
] | 2023-10-23 06:34:33 | http://arxiv.org/abs/2310.14608v1 | http://arxiv.org/pdf/2310.14608v1 | 2310.14608v1 |
Investigating the Fairness of Large Language Models for Predictions on Tabular Data | Recent literature has suggested the potential of using large language models
(LLMs) to make predictions for tabular tasks. However, LLMs have been shown to
exhibit harmful social biases that reflect the stereotypes and inequalities
present in the society. To this end, as well as the widespread use of tabular
data in many high-stake applications, it is imperative to explore the following
questions: what sources of information do LLMs draw upon when making
predictions for tabular tasks; whether and to what extent are LLM predictions
for tabular tasks influenced by social biases and stereotypes; and what are the
consequential implications for fairness? Through a series of experiments, we
delve into these questions and show that LLMs tend to inherit social biases
from their training data which significantly impact their fairness in tabular
prediction tasks. Furthermore, our investigations show that in the context of
bias mitigation, though in-context learning and fine-tuning have a moderate
effect, the fairness metric gap between different subgroups is still larger
than that in traditional machine learning models, such as Random Forest and
shallow Neural Networks. This observation emphasizes that the social biases are
inherent within the LLMs themselves and inherited from their pre-training
corpus, not only from the downstream task datasets. Besides, we demonstrate
that label-flipping of in-context examples can significantly reduce biases,
further highlighting the presence of inherent bias within LLMs. | [
"Yanchen Liu",
"Srishti Gautam",
"Jiaqi Ma",
"Himabindu Lakkaraju"
] | 2023-10-23 06:31:28 | http://arxiv.org/abs/2310.14607v1 | http://arxiv.org/pdf/2310.14607v1 | 2310.14607v1 |
Online Auditing of Information Flow | Modern social media platforms play an important role in facilitating rapid
dissemination of information through their massive user networks. Fake news,
misinformation, and unverifiable facts on social media platforms propagate
disharmony and affect society. In this paper, we consider the problem of online
auditing of information flow/propagation with the goal of classifying news
items as fake or genuine. Specifically, driven by experiential studies on
real-world social media platforms, we propose a probabilistic Markovian
information spread model over networks modeled by graphs. We then formulate our
inference task as a certain sequential detection problem with the goal of
minimizing the combination of the error probability and the time it takes to
achieve correct decision. For this model, we find the optimal detection
algorithm minimizing the aforementioned risk and prove several statistical
guarantees. We then test our algorithm over real-world datasets. To that end,
we first construct an offline algorithm for learning the probabilistic
information spreading model, and then apply our optimal detection algorithm.
Experimental study show that our algorithm outperforms state-of-the-art
misinformation detection algorithms in terms of accuracy and detection time. | [
"Mor Oren-Loberman",
"Vered Azar",
"Wasim Huleihel"
] | 2023-10-23 06:03:55 | http://arxiv.org/abs/2310.14595v1 | http://arxiv.org/pdf/2310.14595v1 | 2310.14595v1 |
Pre-Training LiDAR-Based 3D Object Detectors Through Colorization | Accurate 3D object detection and understanding for self-driving cars heavily
relies on LiDAR point clouds, necessitating large amounts of labeled data to
train. In this work, we introduce an innovative pre-training approach, Grounded
Point Colorization (GPC), to bridge the gap between data and labels by teaching
the model to colorize LiDAR point clouds, equipping it with valuable semantic
cues. To tackle challenges arising from color variations and selection bias, we
incorporate color as "context" by providing ground-truth colors as hints during
colorization. Experimental results on the KITTI and Waymo datasets demonstrate
GPC's remarkable effectiveness. Even with limited labeled data, GPC
significantly improves fine-tuning performance; notably, on just 20% of the
KITTI dataset, GPC outperforms training from scratch with the entire dataset.
In sum, we introduce a fresh perspective on pre-training for 3D object
detection, aligning the objective with the model's intended role and ultimately
advancing the accuracy and efficiency of 3D object detection for autonomous
vehicles. | [
"Tai-Yu Pan",
"Chenyang Ma",
"Tianle Chen",
"Cheng Perng Phoo",
"Katie Z Luo",
"Yurong You",
"Mark Campbell",
"Kilian Q. Weinberger",
"Bharath Hariharan",
"Wei-Lun Chao"
] | 2023-10-23 06:00:24 | http://arxiv.org/abs/2310.14592v1 | http://arxiv.org/pdf/2310.14592v1 | 2310.14592v1 |
GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels | Evaluating the performance of graph neural networks (GNNs) is an essential
task for practical GNN model deployment and serving, as deployed GNNs face
significant performance uncertainty when inferring on unseen and unlabeled test
graphs, due to mismatched training-test graph distributions. In this paper, we
study a new problem, GNN model evaluation, that aims to assess the performance
of a specific GNN model trained on labeled and observed graphs, by precisely
estimating its performance (e.g., node classification accuracy) on unseen
graphs without labels. Concretely, we propose a two-stage GNN model evaluation
framework, including (1) DiscGraph set construction and (2) GNNEvaluator
training and inference. The DiscGraph set captures wide-range and diverse graph
data distribution discrepancies through a discrepancy measurement function,
which exploits the outputs of GNNs related to latent node embeddings and node
class predictions. Under the effective training supervision from the DiscGraph
set, GNNEvaluator learns to precisely estimate node classification accuracy of
the to-be-evaluated GNN model and makes an accurate inference for evaluating
GNN model performance. Extensive experiments on real-world unseen and unlabeled
test graphs demonstrate the effectiveness of our proposed method for GNN model
evaluation. | [
"Xin Zheng",
"Miao Zhang",
"Chunyang Chen",
"Soheila Molaei",
"Chuan Zhou",
"Shirui Pan"
] | 2023-10-23 05:51:59 | http://arxiv.org/abs/2310.14586v1 | http://arxiv.org/pdf/2310.14586v1 | 2310.14586v1 |
JointMatch: A Unified Approach for Diverse and Collaborative Pseudo-Labeling to Semi-Supervised Text Classification | Semi-supervised text classification (SSTC) has gained increasing attention
due to its ability to leverage unlabeled data. However, existing approaches
based on pseudo-labeling suffer from the issues of pseudo-label bias and error
accumulation. In this paper, we propose JointMatch, a holistic approach for
SSTC that addresses these challenges by unifying ideas from recent
semi-supervised learning and the task of learning with noise. JointMatch
adaptively adjusts classwise thresholds based on the learning status of
different classes to mitigate model bias towards current easy classes.
Additionally, JointMatch alleviates error accumulation by utilizing two
differently initialized networks to teach each other in a cross-labeling
manner. To maintain divergence between the two networks for mutual learning, we
introduce a strategy that weighs more disagreement data while also allowing the
utilization of high-quality agreement data for training. Experimental results
on benchmark datasets demonstrate the superior performance of JointMatch,
achieving a significant 5.13% improvement on average. Notably, JointMatch
delivers impressive results even in the extremely-scarce-label setting,
obtaining 86% accuracy on AG News with only 5 labels per class. We make our
code available at https://github.com/HenryPengZou/JointMatch. | [
"Henry Peng Zou",
"Cornelia Caragea"
] | 2023-10-23 05:43:35 | http://arxiv.org/abs/2310.14583v1 | http://arxiv.org/pdf/2310.14583v1 | 2310.14583v1 |
FedSplitX: Federated Split Learning for Computationally-Constrained Heterogeneous Clients | Foundation models (FMs) have demonstrated remarkable performance in machine
learning but demand extensive training data and computational resources.
Federated learning (FL) addresses the challenges posed by FMs, especially
related to data privacy and computational burdens. However, FL on FMs faces
challenges in situations with heterogeneous clients possessing varying
computing capabilities, as clients with limited capabilities may struggle to
train the computationally intensive FMs. To address these challenges, we
propose FedSplitX, a novel FL framework that tackles system heterogeneity.
FedSplitX splits a large model into client-side and server-side components at
multiple partition points to accommodate diverse client capabilities. This
approach enables clients to collaborate while leveraging the server's
computational power, leading to improved model performance compared to
baselines that limit model size to meet the requirement of the poorest client.
Furthermore, FedSplitX incorporates auxiliary networks at each partition point
to reduce communication costs and delays while enhancing model performance. Our
experiments demonstrate that FedSplitX effectively utilizes server capabilities
to train large models, outperforming baseline approaches. | [
"Jiyun Shin",
"Jinhyun Ahn",
"Honggu Kang",
"Joonhyuk Kang"
] | 2023-10-23 05:34:31 | http://arxiv.org/abs/2310.14579v1 | http://arxiv.org/pdf/2310.14579v1 | 2310.14579v1 |
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Dataset Card for "arxiv_cs_papers"
This dataset contains the subset of ArXiv papers with the "cs.LG" tag to indicate the paper is about Machine Learning.
The core dataset is filtered from the full ArXiv dataset hosted on Kaggle: https://www.kaggle.com/datasets/Cornell-University/arxiv. The original dataset contains roughly 2 million papers. This dataset contains roughly 100,000 papers following the category filtering.
The dataset is maintained with requests to the ArXiv API.
The ArXiv dataset contains features:
- title
- abstract
- authors
- published
- url
- pdf_url
- arxiv_id
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