pwc-archive/papers-with-abstracts huggingface dataset:
Decoupling regularization from the action space
Authors: Sobhan Mohammadpour, Emma Frejinger, Pierre-Luc Bacon
Date: 2024-06-10 00:00:00
Tasks: Reinforcement Learning (RL)

Regularized reinforcement learning (RL), particularly the entropy-regularized kind, has gained traction in optimal control and inverse RL. While standard unregularized RL methods remain unaffected by changes in the number of actions, we show that it can severely impact their regularized counterparts. This paper demonstrates the importance of decoupling the regularizer from the action space: that is, to maintain a consistent level of regularization regardless of how many actions are involved to avoid over-regularization. Whereas the problem can be avoided by introducing a task-specific temperature parameter, it is often undesirable and cannot solve the problem when action spaces are state-dependent. In the state-dependent action context, different states with varying action spaces are regularized inconsistently. We introduce two solutions: a static temperature selection approach and a dynamic counterpart, universally applicable where this problem arises. Implementing these changes improves performance on the DeepMind control suite in static and dynamic temperature regimes and a biological sequence design task.
Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs
Authors: Ziang Chen, Xiaohan Chen, Jialin Liu, Xinshang Wang, Wotao Yin
Date: 2024-06-09 00:00:00

Quadratic programming (QP) is the most widely applied category of problems in nonlinear programming. Many applications require real-time/fast solutions, though not necessarily with high precision. Existing methods either involve matrix decomposition or use the preconditioned conjugate gradient method. For relatively large instances, these methods cannot achieve the real-time requirement unless there is an effective precondition. Recently, graph neural networks (GNNs) opened new possibilities for QP. Some promising empirical studies of applying GNNs for QP tasks show that GNNs can capture key characteristics of an optimization instance and provide adaptive guidance accordingly to crucial configurations during the solving process, or directly provide an approximate solution. Despite notable empirical observations, theoretical foundations are still lacking. In this work, we investigate the expressive or representative power of GNNs, a crucial aspect of neural network theory, specifically in the context of QP tasks, with both continuous and mixed-integer settings. We prove the existence of message-passing GNNs that can reliably represent key properties of quadratic programs, including feasibility, optimal objective value, and optimal solution. Our theory is validated by numerical results.
Linear Causal Representation Learning from Unknown Multi-node Interventions
Authors: Burak Varici, Emre Acartürk, Karthikeyan Shanmugam, Ali Tajer
Date: 2024-06-09 00:00:00
Tasks: Representation Learning, valid

Despite the multifaceted recent advances in interventional causal representation learning (CRL), they primarily focus on the stylized assumption of single-node interventions. This assumption is not valid in a wide range of applications, and generally, the subset of nodes intervened in an interventional environment is fully unknown. This paper focuses on interventional CRL under unknown multi-node (UMN) interventional environments and establishes the first identifiability results for general latent causal models (parametric or nonparametric) under stochastic interventions (soft or hard) and linear transformation from the latent to observed space. Specifically, it is established that given sufficiently diverse interventional environments, (i) identifiability up to ancestors is possible using only soft interventions, and (ii) perfect identifiability is possible using hard interventions. Remarkably, these guarantees match the best-known results for more restrictive single-node interventions. Furthermore, CRL algorithms are also provided that achieve the identifiability guarantees. A central step in designing these algorithms is establishing the relationships between UMN interventional CRL and score functions associated with the statistical models of different interventional environments. Establishing these relationships also serves as constructive proof of the identifiability guarantees.
Contrastive Learning from Synthetic Audio Doppelgängers
Authors: Manuel Cherep, Nikhil Singh
Date: 2024-06-09 00:00:00
Tasks: Audio Classification, Contrastive Learning, Diversity

Learning robust audio representations currently demands extensive datasets of real-world sound recordings. By applying artificial transformations to these recordings, models can learn to recognize similarities despite subtle variations through techniques like contrastive learning. However, these transformations are only approximations of the true diversity found in real-world sounds, which are generated by complex interactions of physical processes, from vocal cord vibrations to the resonance of musical instruments. We propose a solution to both the data scale and transformation limitations, leveraging synthetic audio. By randomly perturbing the parameters of a sound synthesizer, we generate audio doppelg\"angers-synthetic positive pairs with causally manipulated variations in timbre, pitch, and temporal envelopes. These variations, difficult to achieve through augmentations of existing audio, provide a rich source of contrastive information. Despite the shift to randomly generated synthetic data, our method produces strong representations, outperforming real data on several standard audio classification tasks. Notably, our approach is lightweight, requires no data storage, and has only a single hyperparameter, which we extensively analyze. We offer this method as a complement to existing strategies for contrastive learning in audio, using synthesized sounds to reduce the data burden on practitioners.
Large Language Models Memorize Sensor Datasets! Implications on Human Activity Recognition Research
Authors: Harish Haresamudram, Hrudhai Rajasekhar, Nikhil Murlidhar Shanbhogue, Thomas Ploetz
Date: 2024-06-09 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Memorization

The astonishing success of Large Language Models (LLMs) in Natural Language Processing (NLP) has spurred their use in many application domains beyond text analysis, including wearable sensor-based Human Activity Recognition (HAR). In such scenarios, often sensor data are directly fed into an LLM along with text instructions for the model to perform activity classification. Seemingly remarkable results have been reported for such LLM-based HAR systems when they are evaluated on standard benchmarks from the field. Yet, we argue, care has to be taken when evaluating LLM-based HAR systems in such a traditional way. Most contemporary LLMs are trained on virtually the entire (accessible) internet -- potentially including standard HAR datasets. With that, it is not unlikely that LLMs actually had access to the test data used in such benchmark experiments.The resulting contamination of training data would render these experimental evaluations meaningless. In this paper we investigate whether LLMs indeed have had access to standard HAR datasets during training. We apply memorization tests to LLMs, which involves instructing the models to extend given snippets of data. When comparing the LLM-generated output to the original data we found a non-negligible amount of matches which suggests that the LLM under investigation seems to indeed have seen wearable sensor data from the benchmark datasets during training. For the Daphnet dataset in particular, GPT-4 is able to reproduce blocks of sensor readings. We report on our investigations and discuss potential implications on HAR research, especially with regards to reporting results on experimental evaluation
Event prediction and causality inference despite incomplete information
Authors: Harrison Lam, Yuanjie Chen, Noboru Kanazawa, Mohammad Chowdhury, Anna Battista, Stephan Waldert
Date: 2024-06-09 00:00:00
Tasks: Prediction, Time Series Prediction

We explored the challenge of predicting and explaining the occurrence of events within sequences of data points. Our focus was particularly on scenarios in which unknown triggers causing the occurrence of events may consist of non-consecutive, masked, noisy data points. This scenario is akin to an agent tasked with learning to predict and explain the occurrence of events without understanding the underlying processes or having access to crucial information. Such scenarios are encountered across various fields, such as genomics, hardware and software verification, and financial time series prediction. We combined analytical, simulation, and machine learning (ML) approaches to investigate, quantify, and provide solutions to this challenge. We deduced and validated equations generally applicable to any variation of the underlying challenge. Using these equations, we (1) described how the level of complexity changes with various parameters (e.g., number of apparent and hidden states, trigger length, confidence, etc.) and (2) quantified the data needed to successfully train an ML model. We then (3) proved our ML solution learns and subsequently identifies unknown triggers and predicts the occurrence of events. If the complexity of the challenge is too high, our ML solution can identify trigger candidates to be used to interactively probe the system under investigation to determine the true trigger in a way considerably more efficient than brute force methods. By sharing our findings, we aim to assist others grappling with similar challenges, enabling estimates on the complexity of their problem, the data required and a solution to solve it.
Security Vulnerability Detection with Multitask Self-Instructed Fine-Tuning of Large Language Models
Authors: Aidan Z. H. Yang, Haoye Tian, He Ye, Ruben Martins, Claire Le Goues
Date: 2024-06-09 00:00:00
Tasks: Graph Neural Network, Vulnerability Detection

Software security vulnerabilities allow attackers to perform malicious activities to disrupt software operations. Recent Transformer-based language models have significantly advanced vulnerability detection, surpassing the capabilities of static analysis based deep learning models. However, language models trained solely on code tokens do not capture either the explanation of vulnerability type or the data flow structure information of code, both of which are crucial for vulnerability detection. We propose a novel technique that integrates a multitask sequence-to-sequence LLM with pro-gram control flow graphs encoded as a graph neural network to achieve sequence-to-classification vulnerability detection. We introduce MSIVD, multitask self-instructed fine-tuning for vulnerability detection, inspired by chain-of-thought prompting and LLM self-instruction. Our experiments demonstrate that MSIVD achieves superior performance, outperforming the highest LLM-based vulnerability detector baseline (LineVul), with a F1 score of 0.92 on the BigVul dataset, and 0.48 on the PreciseBugs dataset. By training LLMs and GNNs simultaneously using a combination of code and explanatory metrics of a vulnerable program, MSIVD represents a promising direction for advancing LLM-based vulnerability detection that generalizes to unseen data. Based on our findings, we further discuss the necessity for new labelled security vulnerability datasets, as recent LLMs have seen or memorized prior datasets' held-out evaluation data.
Information Theoretic Guarantees For Policy Alignment In Large Language Models
Authors: Youssef Mroueh
Date: 2024-06-09 00:00:00

Policy alignment of large language models refers to constrained policy optimization, where the policy is optimized to maximize a reward while staying close to a reference policy with respect to an $f$-divergence such as the $\mathsf{KL}$ divergence. The best of $n$ alignment policy selects a sample from the reference policy that has the maximum reward among $n$ independent samples. For both cases (policy alignment and best of $n$), recent works showed empirically that the reward improvement of the aligned policy on the reference one scales like $\sqrt{\mathsf{KL}}$, with an explicit bound in $n$ on the $\mathsf{KL}$ for the best of $n$ policy. We show in this paper that the $\sqrt{\mathsf{KL}}$ information theoretic upper bound holds if the reward under the reference policy has sub-gaussian tails. Moreover, we prove for the best of $n$ policy, that the $\mathsf{KL}$ upper bound can be obtained for any $f$-divergence via a reduction to exponential order statistics owing to the R\'enyi representation of order statistics, and a data processing inequality. If additional information is known on the tails of the aligned policy we show that tighter control on the reward improvement can be obtained via the R\'enyi divergence. Finally we demonstrate how these upper bounds transfer from proxy rewards to golden rewards which results in a decrease in the golden reward improvement due to overestimation and approximation errors of the proxy reward.
Distributional Preference Alignment of LLMs via Optimal Transport
Authors: Igor Melnyk, Youssef Mroueh, Brian Belgodere, Mattia Rigotti, Apoorva Nitsure, Mikhail Yurochkin, Kristjan Greenewald, Jiri Navratil, Jerret Ross
Date: 2024-06-09 00:00:00

Current LLM alignment techniques use pairwise human preferences at a sample level, and as such, they do not imply an alignment on the distributional level. We propose in this paper Alignment via Optimal Transport (AOT), a novel method for distributional preference alignment of LLMs. AOT aligns LLMs on unpaired preference data by making the reward distribution of the positive samples stochastically dominant in the first order on the distribution of negative samples. We introduce a convex relaxation of this first-order stochastic dominance and cast it as an optimal transport problem with a smooth and convex cost. Thanks to the one-dimensional nature of the resulting optimal transport problem and the convexity of the cost, it has a closed-form solution via sorting on empirical measures. We fine-tune LLMs with this AOT objective, which enables alignment by penalizing the violation of the stochastic dominance of the reward distribution of the positive samples on the reward distribution of the negative samples. We analyze the sample complexity of AOT by considering the dual of the OT problem and show that it converges at the parametric rate. Empirically, we show on a diverse set of alignment datasets and LLMs that AOT leads to state-of-the-art models in the 7B family of models when evaluated with Open LLM Benchmarks and AlpacaEval.
LGR2: Language Guided Reward Relabeling for Accelerating Hierarchical Reinforcement Learning
Authors: Utsav Singh, Pramit Bhattacharyya, Vinay P. Namboodiri
Date: 2024-06-09 00:00:00
Tasks: Code Generation, Hierarchical Reinforcement Learning, In-Context Learning, Logical Reasoning, reinforcement-learning, Reinforcement Learning

Developing interactive systems that utilize natural language instructions to solve complex robotic control tasks has long been a goal of the robotics community. While Large Language Models (LLMs) excel at logical reasoning, in-context learning, and code generation, translating high-level instructions into low-level robotic actions still remains challenging. Furthermore, solving such tasks often requires acquiring policies to execute diverse subtasks and integrating them to achieve the final objective. Hierarchical Reinforcement Learning (HRL) offers a promising solution for solving such tasks by enabling temporal abstraction and improved exploration. However, HRL suffers from non-stationarity caused by the changing lower-level behaviour, which hinders effective policy learning. We propose LGR2, a novel HRL framework that mitigates non-stationarity in HRL by using language-guided higher-level rewards that remain unaffected by the changing lower-level policy behaviour. To analyze the efficacy of our approach, we perform empirical analysis to demonstrate that LGR2 effectively mitigates non-stationarity in HRL and attains success rates exceeding 70% in challenging, sparsely-rewarded robotic navigation and manipulation environments, where other baselines typically fail to show significant progress. Finally, we perform real-world robotic experiments on complex tasks and demonstrate that LGR2 consistently outperforms the baselines.
Zero-Shot End-To-End Spoken Question Answering In Medical Domain
Authors: Yanis Labrak, Adel Moumen, Richard Dufour, Mickael Rouvier
Date: 2024-06-09 00:00:00
Tasks: Answer Selection, Question Answering

In the rapidly evolving landscape of spoken question-answering (SQA), the integration of large language models (LLMs) has emerged as a transformative development. Conventional approaches often entail the use of separate models for question audio transcription and answer selection, resulting in significant resource utilization and error accumulation. To tackle these challenges, we explore the effectiveness of end-to-end (E2E) methodologies for SQA in the medical domain. Our study introduces a novel zero-shot SQA approach, compared to traditional cascade systems. Through a comprehensive evaluation conducted on a new open benchmark of 8 medical tasks and 48 hours of synthetic audio, we demonstrate that our approach requires up to 14.7 times fewer resources than a combined 1.3B parameters LLM with a 1.55B parameters ASR model while improving average accuracy by 0.5\%. These findings underscore the potential of E2E methodologies for SQA in resource-constrained contexts.
Machine Against the RAG: Jamming Retrieval-Augmented Generation with Blocker Documents
Authors: Avital Shafran, Roei Schuster, Vitaly Shmatikov
Date: 2024-06-09 00:00:00
Tasks: RAG, Retrieval, Retrieval-augmented Generation

Retrieval-augmented generation (RAG) systems respond to queries by retrieving relevant documents from a knowledge database and applying an LLM to the retrieved documents. We demonstrate that RAG systems that operate on databases with untrusted content are vulnerable to denial-of-service attacks we call jamming. An adversary can add a single ``blocker'' document to the database that will be retrieved in response to a specific query and result in the RAG system not answering this query, ostensibly because it lacks relevant information or because the answer is unsafe. We describe and measure the efficacy of several methods for generating blocker documents, including a new method based on black-box optimization. Our method (1) does not rely on instruction injection, (2) does not require the adversary to know the embedding or LLM used by the target RAG system, and (3) does not employ an auxiliary LLM. We evaluate jamming attacks on several embeddings and LLMs and demonstrate that the existing safety metrics for LLMs do not capture their vulnerability to jamming. We then discuss defenses against blocker documents.
Source -Free Domain Adaptation for Speaker Verification in Data-Scarce Languages and Noisy Channels
Authors: Shlomo Salo Elia, Aviad Malachi, Vered Aharonson, Gadi Pinkas
Date: 2024-06-09 00:00:00
Tasks: Domain Adaptation, Source-Free Domain Adaptation, Speaker Verification

Domain adaptation is often hampered by exceedingly small target datasets and inaccessible source data. These conditions are prevalent in speech verification, where privacy policies and/or languages with scarce speech resources limit the availability of sufficient data. This paper explored techniques of sourcefree domain adaptation unto a limited target speech dataset for speaker verificationin data-scarce languages. Both language and channel mis-match between source and target were investigated. Fine-tuning methods were evaluated and compared across different sizes of labeled target data. A novel iterative cluster-learn algorithm was studied for unlabeled target datasets.
Improving Antibody Design with Force-Guided Sampling in Diffusion Models
Authors: Paulina Kulytė, Francisco Vargas, Simon Valentin Mathis, Yu Guang Wang, José Miguel Hernández-Lobato, Pietro Liò
Date: 2024-06-09 00:00:00
Tasks: Denoising, Specificity

Antibodies, crucial for immune defense, primarily rely on complementarity-determining regions (CDRs) to bind and neutralize antigens, such as viruses. The design of these CDRs determines the antibody's affinity and specificity towards its target. Generative models, particularly denoising diffusion probabilistic models (DDPMs), have shown potential to advance the structure-based design of CDR regions. However, only a limited dataset of bound antibody-antigen structures is available, and generalization to out-of-distribution interfaces remains a challenge. Physics based force-fields, which approximate atomic interactions, offer a coarse but universal source of information to better mold designs to target interfaces. Integrating this foundational information into diffusion models is, therefore, highly desirable. Here, we propose a novel approach to enhance the sampling process of diffusion models by integrating force field energy-based feedback. Our model, DiffForce, employs forces to guide the diffusion sampling process, effectively blending the two distributions. Through extensive experiments, we demonstrate that our method guides the model to sample CDRs with lower energy, enhancing both the structure and sequence of the generated antibodies.
Probabilistic Approach to Black-Box Binary Optimization with Budget Constraints: Application to Sensor Placement
Authors: Ahmed Attia
Date: 2024-06-09 00:00:00

We present a fully probabilistic approach for solving binary optimization problems with black-box objective functions and with budget constraints. In the probabilistic approach, the optimization variable is viewed as a random variable and is associated with a parametric probability distribution. The original optimization problem is replaced with an optimization over the expected value of the original objective, which is then optimized over the probability distribution parameters. The resulting optimal parameter (optimal policy) is used to sample the binary space to produce estimates of the optimal solution(s) of the original binary optimization problem. The probability distribution is chosen from the family of Bernoulli models because the optimization variable is binary. The optimization constraints generally restrict the feasibility region. This can be achieved by modeling the random variable with a conditional distribution given satisfiability of the constraints. Thus, in this work we develop conditional Bernoulli distributions to model the random variable conditioned by the total number of nonzero entries, that is, the budget constraint. This approach (a) is generally applicable to binary optimization problems with nonstochastic black-box objective functions and budget constraints; (b) accounts for budget constraints by employing conditional probabilities that sample only the feasible region and thus considerably reduces the computational cost compared with employing soft constraints; and (c) does not employ soft constraints and thus does not require tuning of a regularization parameter, for example to promote sparsity, which is challenging in sensor placement optimization problems. The proposed approach is verified numerically by using an idealized bilinear binary optimization problem and is validated by using a sensor placement experiment in a parameter identification setup.