pwc-archive/papers-with-abstracts huggingface dataset:
Q-MARL: A quantum-inspired algorithm using neural message passing for large-scale multi-agent reinforcement learning
Authors: Kha Vo, Chin-Teng Lin
Date: 2025-03-10 00:00:00
Tasks: Multi-agent Reinforcement Learning

Inspired by a graph-based technique for predicting molecular properties in quantum chemistry -- atoms' position within molecules in three-dimensional space -- we present Q-MARL, a completely decentralised learning architecture that supports very large-scale multi-agent reinforcement learning scenarios without the need for strong assumptions like common rewards or agent order. The key is to treat each agent as relative to its surrounding agents in an environment that is presumed to change dynamically. Hence, in each time step, an agent is the centre of its own neighbourhood and also a neighbour to many other agents. Each role is formulated as a sub-graph, and each sub-graph is used as a training sample. A message-passing neural network supports full-scale vertex and edge interaction within a local neighbourhood, while a parameter governing the depth of the sub-graphs eases the training burden. During testing, an agent's actions are locally ensembled across all the sub-graphs that contain it, resulting in robust decisions. Where other approaches struggle to manage 50 agents, Q-MARL can easily marshal thousands. A detailed theoretical analysis proves improvement and convergence, and simulations with the typical collaborative and competitive scenarios show dramatically faster training speeds and reduced training losses.
Inorganic Catalyst Efficiency Prediction Based on EAPCR Model: A Deep Learning Solution for Multi-Source Heterogeneous Data
Authors: Zhangdi Liu, Ling An, Mengke Song, Zhuohang Yu, Shan Wang, Kezhen Qi, Zhenyu Zhang, ChiChun Zhou
Date: 2025-03-10 00:00:00
Tasks: Deep Learning

The design of inorganic catalysts and the prediction of their catalytic efficiency are fundamental challenges in chemistry and materials science. Traditional catalyst evaluation methods primarily rely on machine learning techniques; however, these methods often struggle to process multi-source heterogeneous data, limiting both predictive accuracy and generalization. To address these limitations, this study introduces the Embedding-Attention-Permutated CNN-Residual (EAPCR) deep learning model. EAPCR constructs a feature association matrix using embedding and attention mechanisms and enhances predictive performance through permutated CNN architectures and residual connections. This approach enables the model to accurately capture complex feature interactions across various catalytic conditions, leading to precise efficiency predictions. EAPCR serves as a powerful tool for computational researchers while also assisting domain experts in optimizing catalyst design, effectively bridging the gap between data-driven modeling and experimental applications. We evaluate EAPCR on datasets from TiO2 photocatalysis, thermal catalysis, and electrocatalysis, demonstrating its superiority over traditional machine learning methods (e.g., linear regression, random forest) as well as conventional deep learning models (e.g., ANN, NNs). Across multiple evaluation metrics (MAE, MSE, R2, and RMSE), EAPCR consistently outperforms existing approaches. These findings highlight the strong potential of EAPCR in inorganic catalytic efficiency prediction. As a versatile deep learning framework, EAPCR not only improves predictive accuracy but also establishes a solid foundation for future large-scale model development in inorganic catalysis.
RePO: ReLU-based Preference Optimization
Authors: Junkang Wu, Kexin Huang, Xue Wang, Jinyang Gao, Bolin Ding, Jiancan Wu, Xiangnan He, Xiang Wang
Date: 2025-03-10 00:00:00

Aligning large language models (LLMs) with human preferences is critical for real-world deployment, yet existing methods like RLHF face computational and stability challenges. While DPO establishes an offline paradigm with single hyperparameter $\beta$, subsequent methods like SimPO reintroduce complexity through dual parameters ($\beta$, $\gamma$). We propose {ReLU-based Preference Optimization (RePO)}, a streamlined algorithm that eliminates $\beta$ via two advances: (1) retaining SimPO's reference-free margins but removing $\beta$ through gradient analysis, and (2) adopting a ReLU-based max-margin loss that naturally filters trivial pairs. Theoretically, RePO is characterized as SimPO's limiting case ($\beta \to \infty$), where the logistic weighting collapses to binary thresholding, forming a convex envelope of the 0-1 loss. Empirical results on AlpacaEval 2 and Arena-Hard show that RePO outperforms DPO and SimPO across multiple base models, requiring only one hyperparameter to tune.
Sample Complexity of Nonparametric Closeness Testing for Continuous Distributions and Its Application to Causal Discovery with Hidden Confounding
Authors: Fateme Jamshidi, Sina Akbari, Negar Kiyavash
Date: 2025-03-10 00:00:00
Tasks: Causal Discovery

We study the problem of closeness testing for continuous distributions and its implications for causal discovery. Specifically, we analyze the sample complexity of distinguishing whether two multidimensional continuous distributions are identical or differ by at least $\epsilon$ in terms of Kullback-Leibler (KL) divergence under non-parametric assumptions. To this end, we propose an estimator of KL divergence which is based on the von Mises expansion. Our closeness test attains optimal parametric rates under smoothness assumptions. Equipped with this test, which serves as a building block of our causal discovery algorithm to identify the causal structure between two multidimensional random variables, we establish sample complexity guarantees for our causal discovery method. To the best of our knowledge, this work is the first work that provides sample complexity guarantees for distinguishing cause and effect in multidimensional non-linear models with non-Gaussian continuous variables in the presence of unobserved confounding.
Efficient Membership Inference Attacks by Bayesian Neural Network
Authors: Zhenlong Liu, Wenyu Jiang, Feng Zhou, Hongxin Wei
Date: 2025-03-10 00:00:00
Tasks: Bayesian Inference, Inference Attack, Membership Inference Attack, quantile regression

Membership Inference Attacks (MIAs) aim to estimate whether a specific data point was used in the training of a given model. Previous attacks often utilize multiple reference models to approximate the conditional score distribution, leading to significant computational overhead. While recent work leverages quantile regression to estimate conditional thresholds, it fails to capture epistemic uncertainty, resulting in bias in low-density regions. In this work, we propose a novel approach - Bayesian Membership Inference Attack (BMIA), which performs conditional attack through Bayesian inference. In particular, we transform a trained reference model into Bayesian neural networks by Laplace approximation, enabling the direct estimation of the conditional score distribution by probabilistic model parameters. Our method addresses both epistemic and aleatoric uncertainty with only a reference model, enabling efficient and powerful MIA. Extensive experiments on five datasets demonstrate the effectiveness and efficiency of BMIA.
Trustworthy Machine Learning via Memorization and the Granular Long-Tail: A Survey on Interactions, Tradeoffs, and Beyond
Authors: Qiongxiu Li, Xiaoyu Luo, Yiyi Chen, Johannes Bjerva
Date: 2025-03-10 00:00:00
Tasks: Attribute, Fairness, Memorization

The role of memorization in machine learning (ML) has garnered significant attention, particularly as modern models are empirically observed to memorize fragments of training data. Previous theoretical analyses, such as Feldman's seminal work, attribute memorization to the prevalence of long-tail distributions in training data, proving it unavoidable for samples that lie in the tail of the distribution. However, the intersection of memorization and trustworthy ML research reveals critical gaps. While prior research in memorization in trustworthy ML has solely focused on class imbalance, recent work starts to differentiate class-level rarity from atypical samples, which are valid and rare intra-class instances. However, a critical research gap remains: current frameworks conflate atypical samples with noisy and erroneous data, neglecting their divergent impacts on fairness, robustness, and privacy. In this work, we conduct a thorough survey of existing research and their findings on trustworthy ML and the role of memorization. More and beyond, we identify and highlight uncharted gaps and propose new revenues in this research direction. Since existing theoretical and empirical analyses lack the nuances to disentangle memorization's duality as both a necessity and a liability, we formalize three-level long-tail granularity - class imbalance, atypicality, and noise - to reveal how current frameworks misapply these levels, perpetuating flawed solutions. By systematizing this granularity, we draw a roadmap for future research. Trustworthy ML must reconcile the nuanced trade-offs between memorizing atypicality for fairness assurance and suppressing noise for robustness and privacy guarantee. Redefining memorization via this granularity reshapes the theoretical foundation for trustworthy ML, and further affords an empirical prerequisite for models that align performance with societal trust.
ADROIT: A Self-Supervised Framework for Learning Robust Representations for Active Learning
Authors: Soumya Banerjee, Vinay Kumar Verma
Date: 2025-03-10 00:00:00
Tasks: Active Learning, Knowledge Distillation, Representation Learning

Active learning aims to select optimal samples for labeling, minimizing annotation costs. This paper introduces a unified representation learning framework tailored for active learning with task awareness. It integrates diverse sources, comprising reconstruction, adversarial, self-supervised, knowledge-distillation, and classification losses into a unified VAE-based ADROIT approach. The proposed approach comprises three key components - a unified representation generator (VAE), a state discriminator, and a (proxy) task-learner or classifier. ADROIT learns a latent code using both labeled and unlabeled data, incorporating task-awareness by leveraging labeled data with the proxy classifier. Unlike previous approaches, the proxy classifier additionally employs a self-supervised loss on unlabeled data and utilizes knowledge distillation to align with the target task-learner. The state discriminator distinguishes between labeled and unlabeled data, facilitating the selection of informative unlabeled samples. The dynamic interaction between VAE and the state discriminator creates a competitive environment, with the VAE attempting to deceive the discriminator, while the state discriminator learns to differentiate between labeled and unlabeled inputs. Extensive evaluations on diverse datasets and ablation analysis affirm the effectiveness of the proposed model.
Federated Multimodal Learning with Dual Adapters and Selective Pruning for Communication and Computational Efficiency
Authors: Duy Phuong Nguyen, J. Pablo Munoz, Tanya Roosta, Ali Jannesari
Date: 2025-03-10 00:00:00
Tasks: Computational Efficiency, Federated Learning

Federated Learning (FL) enables collaborative learning across distributed clients while preserving data privacy. However, FL faces significant challenges when dealing with heterogeneous data distributions, which can lead to suboptimal global models that fail to generalize across diverse clients. In this work, we propose a novel framework designed to tackle these challenges by introducing a dual-adapter approach. The method utilizes a larger local adapter for client-specific personalization and a smaller global adapter to facilitate efficient knowledge sharing across clients. Additionally, we incorporate a pruning mechanism to reduce communication overhead by selectively removing less impactful parameters from the local adapter. Through extensive experiments on a range of vision and language tasks, our method demonstrates superior performance compared to existing approaches. It achieves higher test accuracy, lower performance variance among clients, and improved worst-case performance, all while significantly reducing communication and computation costs. Overall, the proposed method addresses the critical trade-off between model personalization and generalization, offering a scalable solution for real-world FL applications.
An Empirical Comparison of Cost Functions in Inductive Logic Programming
Authors: Céline Hocquette, Andrew Cropper
Date: 2025-03-10 00:00:00
Tasks: Inductive logic programming, Program Synthesis

Recent inductive logic programming (ILP) approaches learn optimal hypotheses. An optimal hypothesis minimises a given cost function on the training data. There are many cost functions, such as minimising training error, textual complexity, or the description length of hypotheses. However, selecting an appropriate cost function remains a key question. To address this gap, we extend a constraint-based ILP system to learn optimal hypotheses for seven standard cost functions. We then empirically compare the generalisation error of optimal hypotheses induced under these standard cost functions. Our results on over 20 domains and 1000 tasks, including game playing, program synthesis, and image reasoning, show that, while no cost function consistently outperforms the others, minimising training error or description length has the best overall performance. Notably, our results indicate that minimising the size of hypotheses does not always reduce generalisation error.
Graph-Dependent Regret Bounds in Multi-Armed Bandits with Interference
Authors: Fateme Jamshidi, Mohammad ShahverdiKondori, Negar Kiyavash
Date: 2025-03-10 00:00:00
Tasks: Multi-Armed Bandits, Sequential Decision Making

Multi-armed bandits (MABs) are frequently used for online sequential decision-making in applications ranging from recommending personalized content to assigning treatments to patients. A recurring challenge in the applicability of the classic MAB framework to real-world settings is ignoring \textit{interference}, where a unit's outcome depends on treatment assigned to others. This leads to an exponentially growing action space, rendering standard approaches computationally impractical. We study the MAB problem under network interference, where each unit's reward depends on its own treatment and those of its neighbors in a given interference graph. We propose a novel algorithm that uses the local structure of the interference graph to minimize regret. We derive a graph-dependent upper bound on cumulative regret showing that it improves over prior work. Additionally, we provide the first lower bounds for bandits with arbitrary network interference, where each bound involves a distinct structural property of the interference graph. These bounds demonstrate that when the graph is either dense or sparse, our algorithm is nearly optimal, with upper and lower bounds that match up to logarithmic factors. We complement our theoretical results with numerical experiments, which show that our approach outperforms baseline methods.
Inductive Moment Matching
Authors: Linqi Zhou, Stefano Ermon, Jiaming Song
Date: 2025-03-10 00:00:00

Diffusion models and Flow Matching generate high-quality samples but are slow at inference, and distilling them into few-step models often leads to instability and extensive tuning. To resolve these trade-offs, we propose Inductive Moment Matching (IMM), a new class of generative models for one- or few-step sampling with a single-stage training procedure. Unlike distillation, IMM does not require pre-training initialization and optimization of two networks; and unlike Consistency Models, IMM guarantees distribution-level convergence and remains stable under various hyperparameters and standard model architectures. IMM surpasses diffusion models on ImageNet-256x256 with 1.99 FID using only 8 inference steps and achieves state-of-the-art 2-step FID of 1.98 on CIFAR-10 for a model trained from scratch.
Denoising Score Distillation: From Noisy Diffusion Pretraining to One-Step High-Quality Generation
Authors: Tianyu Chen, Yasi Zhang, Zhendong Wang, Ying Nian Wu, Oscar Leong, Mingyuan Zhou
Date: 2025-03-10 00:00:00
Tasks: Denoising

Diffusion models have achieved remarkable success in generating high-resolution, realistic images across diverse natural distributions. However, their performance heavily relies on high-quality training data, making it challenging to learn meaningful distributions from corrupted samples. This limitation restricts their applicability in scientific domains where clean data is scarce or costly to obtain. In this work, we introduce denoising score distillation (DSD), a surprisingly effective and novel approach for training high-quality generative models from low-quality data. DSD first pretrains a diffusion model exclusively on noisy, corrupted samples and then distills it into a one-step generator capable of producing refined, clean outputs. While score distillation is traditionally viewed as a method to accelerate diffusion models, we show that it can also significantly enhance sample quality, particularly when starting from a degraded teacher model. Across varying noise levels and datasets, DSD consistently improves generative performancewe summarize our empirical evidence in Fig. 1. Furthermore, we provide theoretical insights showing that, in a linear model setting, DSD identifies the eigenspace of the clean data distributions covariance matrix, implicitly regularizing the generator. This perspective reframes score distillation as not only a tool for efficiency but also a mechanism for improving generative models, particularly in low-quality data settings.
Neural Combinatorial Optimization via Preference Optimization
Authors: Zijun Liao, Jinbiao Chen, Debing Wang, Zizhen Zhang, Jiahai Wang
Date: 2025-03-10 00:00:00
Tasks: Combinatorial Optimization, Job Shop Scheduling, Scheduling

Neural Combinatorial Optimization (NCO) has emerged as a promising approach for NP-hard problems. However, prevailing RL-based methods suffer from low sample efficiency due to sparse rewards and underused solutions. We propose Preference Optimization for Combinatorial Optimization (POCO), a training paradigm that leverages solution preferences via objective values. It introduces: (1) an efficient preference pair construction for better explore and exploit solutions, and (2) a novel loss function that adaptively scales gradients via objective differences, removing reliance on reward models or reference policies. Experiments on Job-Shop Scheduling (JSP), Traveling Salesman (TSP), and Flexible Job-Shop Scheduling (FJSP) show POCO outperforms state-of-the-art neural methods, reducing optimality gaps impressively with efficient inference. POCO is architecture-agnostic, enabling seamless integration with existing NCO models, and establishes preference optimization as a principled framework for combinatorial optimization.
Denoising Hamiltonian Network for Physical Reasoning
Authors: Congyue Deng, Brandon Y. Feng, Cecilia Garraffo, Alan Garbarz, Robin Walters, William T. Freeman, Leonidas Guibas, Kaiming He
Date: 2025-03-10 00:00:00
Tasks: Denoising, Numerical Integration

Machine learning frameworks for physical problems must capture and enforce physical constraints that preserve the structure of dynamical systems. Many existing approaches achieve this by integrating physical operators into neural networks. While these methods offer theoretical guarantees, they face two key limitations: (i) they primarily model local relations between adjacent time steps, overlooking longer-range or higher-level physical interactions, and (ii) they focus on forward simulation while neglecting broader physical reasoning tasks. We propose the Denoising Hamiltonian Network (DHN), a novel framework that generalizes Hamiltonian mechanics operators into more flexible neural operators. DHN captures non-local temporal relationships and mitigates numerical integration errors through a denoising mechanism. DHN also supports multi-system modeling with a global conditioning mechanism. We demonstrate its effectiveness and flexibility across three diverse physical reasoning tasks with distinct inputs and outputs.
Preserving clusters and correlations: a dimensionality reduction method for exceptionally high global structure preservation
Authors: Jacob Gildenblat, Jens Pahnke
Date: 2025-03-10 00:00:00
Tasks: Dimensionality Reduction

We present Preserving Clusters and Correlations (PCC), a novel dimensionality reduction (DR) method a novel dimensionality reduction (DR) method that achieves state-of-the-art global structure (GS) preservation while maintaining competitive local structure (LS) preservation. It optimizes two objectives: a GS preservation objective that preserves an approximation of Pearson and Spearman correlations between high- and low-dimensional distances, and an LS preservation objective that ensures clusters in the high-dimensional data are separable in the low-dimensional data. PCC has a state-of-the-art ability to preserve the GS while having competitive LS preservation. In addition, we show the correlation objective can be combined with UMAP to significantly improve its GS preservation with minimal degradation of the LS. We quantitatively benchmark PCC against existing methods and demonstrate its utility in medical imaging, and show PCC is a competitive DR technique that demonstrates superior GS preservation in our benchmarks.
SEAFL: Enhancing Efficiency in Semi-Asynchronous Federated Learning through Adaptive Aggregation and Selective Training
Authors: Md Sirajul Islam, Sanjeev Panta, Fei Xu, Xu Yuan, Li Chen, Nian-Feng Tzeng
Date: 2025-02-22 00:00:00
Tasks: Federated Learning

Federated Learning (FL) is a promising distributed machine learning framework that allows collaborative learning of a global model across decentralized devices without uploading their local data. However, in real-world FL scenarios, the conventional synchronous FL mechanism suffers from inefficient training caused by slow-speed devices, commonly known as stragglers, especially in heterogeneous communication environments. Though asynchronous FL effectively tackles the efficiency challenge, it induces substantial system overheads and model degradation. Striking for a balance, semi-asynchronous FL has gained increasing attention, while still suffering from the open challenge of stale models, where newly arrived updates are calculated based on outdated weights that easily hurt the convergence of the global model. In this paper, we present {\em SEAFL}, a novel FL framework designed to mitigate both the straggler and the stale model challenges in semi-asynchronous FL. {\em SEAFL} dynamically assigns weights to uploaded models during aggregation based on their staleness and importance to the current global model. We theoretically analyze the convergence rate of {\em SEAFL} and further enhance the training efficiency with an extended variant that allows partial training on slower devices, enabling them to contribute to global aggregation while reducing excessive waiting times. We evaluate the effectiveness of {\em SEAFL} through extensive experiments on three benchmark datasets. The experimental results demonstrate that {\em SEAFL} outperforms its closest counterpart by up to $\sim$22\% in terms of the wall-clock training time required to achieve target accuracy.