pwc-archive/papers-with-abstracts huggingface dataset:
A Generalized Theory of Mixup for Structure-Preserving Synthetic Data
Authors: Chungpa Lee, Jongho Im, Joseph H. T. Kim
Date: 2025-03-03 00:00:00
Tasks: Data Augmentation

Mixup is a widely adopted data augmentation technique known for enhancing the generalization of machine learning models by interpolating between data points. Despite its success and popularity, limited attention has been given to understanding the statistical properties of the synthetic data it generates. In this paper, we delve into the theoretical underpinnings of mixup, specifically its effects on the statistical structure of synthesized data. We demonstrate that while mixup improves model performance, it can distort key statistical properties such as variance, potentially leading to unintended consequences in data synthesis. To address this, we propose a novel mixup method that incorporates a generalized and flexible weighting scheme, better preserving the original data's structure. Through theoretical developments, we provide conditions under which our proposed method maintains the (co)variance and distributional properties of the original dataset. Numerical experiments confirm that the new approach not only preserves the statistical characteristics of the original data but also sustains model performance across repeated synthesis, alleviating concerns of model collapse identified in previous research.
Classifying States of the Hopfield Network with Improved Accuracy, Generalization, and Interpretability
Authors: Hayden McAlister, Anthony Robins, Lech Szymanski
Date: 2025-03-04 00:00:00

We extend the existing work on Hopfield network state classification, employing more complex models that remain interpretable, such as densely-connected feed-forward deep neural networks and support vector machines. The states of the Hopfield network can be grouped into several classes, including learned (those presented during training), spurious (stable states that were not learned), and prototype (stable states that were not learned but are representative for a subset of learned states). It is often useful to determine to what class a given state belongs to; for example to ignore spurious states when retrieving from the network. Previous research has approached the state classification task with simple linear methods, most notably the stability ratio. We deepen the research on classifying states from prototype-regime Hopfield networks, investigating how varying the factors strengthening prototypes influences the state classification task. We study the generalizability of different classification models when trained on states derived from different prototype tasks -- for example, can a network trained on a Hopfield network with 10 prototypes classify states from a network with 20 prototypes? We find that simple models often outperform the stability ratio while remaining interpretable. These models require surprisingly little training data and generalize exceptionally well to states generated by a range of Hopfield networks, even those that were trained on exceedingly different datasets.
From Infants to AI: Incorporating Infant-like Learning in Models Boosts Efficiency and Generalization in Learning Social Prediction Tasks
Authors: Shify Treger, Shimon Ullman
Date: 2025-03-05 00:00:00
Tasks: Human-Object Interaction Detection

Early in development, infants learn a range of useful concepts, which can be challenging from a computational standpoint. This early learning comes together with an initial understanding of aspects of the meaning of concepts, e.g., their implications, causality, and using them to predict likely future events. All this is accomplished in many cases with little or no supervision, and from relatively few examples, compared with current network models. In learning about objects and human-object interactions, early acquired and possibly innate concepts are often used in the process of learning additional, more complex concepts. In the current work, we model how early-acquired concepts are used in the learning of subsequent concepts, and compare the results with standard deep network modeling. We focused in particular on the use of the concepts of animacy and goal attribution in learning to predict future events. We show that the use of early concepts in the learning of new concepts leads to better learning (higher accuracy) and more efficient learning (requiring less data). We further show that this integration of early and new concepts shapes the representation of the concepts acquired by the model. The results show that when the concepts were learned in a human-like manner, the emerging representation was more useful, as measured in terms of generalization to novel data and tasks. On a more general level, the results suggest that there are likely to be basic differences in the conceptual structures acquired by current network models compared to human learning.
Straight-Line Diffusion Model for Efficient 3D Molecular Generation
Authors: Yuyan Ni, Shikun Feng, Haohan Chi, Bowen Zheng, Huan-ang Gao, Wei-Ying Ma, Zhi-Ming Ma, Yanyan Lan
Date: 2025-03-04 00:00:00
Tasks: 3D Molecule Generation, Image Generation, valid

Diffusion-based models have shown great promise in molecular generation but often require a large number of sampling steps to generate valid samples. In this paper, we introduce a novel Straight-Line Diffusion Model (SLDM) to tackle this problem, by formulating the diffusion process to follow a linear trajectory. The proposed process aligns well with the noise sensitivity characteristic of molecular structures and uniformly distributes reconstruction effort across the generative process, thus enhancing learning efficiency and efficacy. Consequently, SLDM achieves state-of-the-art performance on 3D molecule generation benchmarks, delivering a 100-fold improvement in sampling efficiency. Furthermore, experiments on toy data and image generation tasks validate the generality and robustness of SLDM, showcasing its potential across diverse generative modeling domains.
Out-of-Distribution Generalization on Graphs via Progressive Inference
Authors: Yiming Xu, Bin Shi, Zhen Peng, Huixiang Liu, Bo Dong, Chen Chen
Date: 2025-03-04 00:00:00
Tasks: counterfactual, Out-of-Distribution Generalization

The development and evaluation of graph neural networks (GNNs) generally follow the independent and identically distributed (i.i.d.) assumption. Yet this assumption is often untenable in practice due to the uncontrollable data generation mechanism. In particular, when the data distribution shows a significant shift, most GNNs would fail to produce reliable predictions and may even make decisions randomly. One of the most promising solutions to improve the model generalization is to pick out causal invariant parts in the input graph. Nonetheless, we observe a significant distribution gap between the causal parts learned by existing methods and the ground truth, leading to undesirable performance. In response to the above issues, this paper presents GPro, a model that learns graph causal invariance with progressive inference. Specifically, the complicated graph causal invariant learning is decomposed into multiple intermediate inference steps from easy to hard, and the perception of GPro is continuously strengthened through a progressive inference process to extract causal features that are stable to distribution shifts. We also enlarge the training distribution by creating counterfactual samples to enhance the capability of the GPro in capturing the causal invariant parts. Extensive experiments demonstrate that our proposed GPro outperforms the state-of-the-art methods by 4.91% on average. For datasets with more severe distribution shifts, the performance improvement can be up to 6.86%.
Hierarchical Refinement: Optimal Transport to Infinity and Beyond
Authors: Peter Halmos, Julian Gold, Xinhao Liu, Benjamin J. Raphael
Date: 2025-03-04 00:00:00

Optimal transport (OT) has enjoyed great success in machine-learning as a principled way to align datasets via a least-cost correspondence. This success was driven in large part by the runtime efficiency of the Sinkhorn algorithm [Cuturi 2013], which computes a coupling between points from two datasets. However, Sinkhorn has quadratic space complexity in the number of points, limiting the scalability to larger datasets. Low-rank OT achieves linear-space complexity, but by definition, cannot compute a one-to-one correspondence between points. When the optimal transport problem is an assignment problem between datasets then the optimal mapping, known as the Monge map, is guaranteed to be a bijection. In this setting, we show that the factors of an optimal low-rank coupling co-cluster each point with its image under the Monge map. We leverage this invariant to derive an algorithm, Hierarchical Refinement (HiRef), that dynamically constructs a multiscale partition of a dataset using low-rank OT subproblems, culminating in a bijective coupling. Hierarchical Refinement uses linear space and has log-linear runtime, retaining the space advantage of low-rank OT while overcoming its limited resolution. We demonstrate the advantages of Hierarchical Refinement on several datasets, including ones containing over a million points, scaling full-rank OT to problems previously beyond Sinkhorn's reach.
LLM Misalignment via Adversarial RLHF Platforms
Authors: Erfan Entezami, Ali Naseh
Date: 2025-03-04 00:00:00

Reinforcement learning has shown remarkable performance in aligning language models with human preferences, leading to the rise of attention towards developing RLHF platforms. These platforms enable users to fine-tune models without requiring any expertise in developing complex machine learning algorithms. While these platforms offer useful features such as reward modeling and RLHF fine-tuning, their security and reliability remain largely unexplored. Given the growing adoption of RLHF and open-source RLHF frameworks, we investigate the trustworthiness of these systems and their potential impact on behavior of LLMs. In this paper, we present an attack targeting publicly available RLHF tools. In our proposed attack, an adversarial RLHF platform corrupts the LLM alignment process by selectively manipulating data samples in the preference dataset. In this scenario, when a user's task aligns with the attacker's objective, the platform manipulates a subset of the preference dataset that contains samples related to the attacker's target. This manipulation results in a corrupted reward model, which ultimately leads to the misalignment of the language model. Our results demonstrate that such an attack can effectively steer LLMs toward undesirable behaviors within the targeted domains. Our work highlights the critical need to explore the vulnerabilities of RLHF platforms and their potential to cause misalignment in LLMs during the RLHF fine-tuning process.
Graph Transformer with Disease Subgraph Positional Encoding for Improved Comorbidity Prediction
Authors: Xihan Qin, Li Liao
Date: 2025-03-04 00:00:00

Comorbidity, the co-occurrence of multiple medical conditions in a single patient, profoundly impacts disease management and outcomes. Understanding these complex interconnections is crucial, especially in contexts where comorbidities exacerbate outcomes. Leveraging insights from the human interactome (HI) and advancements in graph-based methodologies, this study introduces Transformer with Subgraph Positional Encoding (TSPE) for disease comorbidity prediction. Inspired by Biologically Supervised Embedding (BSE), TSPE employs Transformer's attention mechanisms and Subgraph Positional Encoding (SPE) to capture interactions between nodes and disease associations. Our proposed SPE proves more effective than LPE, as used in Dwivedi et al.'s Graph Transformer, underscoring the importance of integrating clustering and disease-specific information for improved predictive accuracy. Evaluated on real clinical benchmark datasets (RR0 and RR1), TSPE demonstrates substantial performance enhancements over the state-of-the-art method, achieving up to 28.24% higher ROC AUC and 4.93% higher accuracy. This method shows promise for adaptation to other complex graph-based tasks and applications. The source code is available in the GitHub repository at: https://github.com/xihan-qin/TSPE-GraphTransformer.
A2Perf: Real-World Autonomous Agents Benchmark
Authors: Ikechukwu Uchendu, Jason Jabbour, Korneel Van den Berghe, Joel Runevic, Matthew Stewart, Jeffrey Ma, Srivatsan Krishnan, Izzeddin Gur, Austin Huang, Colton Bishop, Paige Bailey, Wenjie Jiang, Ebrahim M. Songhori, Sergio Guadarrama, Jie Tan, Jordan K. Terry, Aleksandra Faust, Vijay Janapa Reddi
Date: 2025-03-04 00:00:00
Tasks: Benchmarking, Combinatorial Optimization, Imitation Learning

Autonomous agents and systems cover a number of application areas, from robotics and digital assistants to combinatorial optimization, all sharing common, unresolved research challenges. It is not sufficient for agents to merely solve a given task; they must generalize to out-of-distribution tasks, perform reliably, and use hardware resources efficiently during training and inference, among other requirements. Several methods, such as reinforcement learning and imitation learning, are commonly used to tackle these problems, each with different trade-offs. However, there is a lack of benchmarking suites that define the environments, datasets, and metrics which can be used to provide a meaningful way for the community to compare progress on applying these methods to real-world problems. We introduce A2Perf--a benchmark with three environments that closely resemble real-world domains: computer chip floorplanning, web navigation, and quadruped locomotion. A2Perf provides metrics that track task performance, generalization, system resource efficiency, and reliability, which are all critical to real-world applications. Using A2Perf, we demonstrate that web navigation agents can achieve latencies comparable to human reaction times on consumer hardware, reveal reliability trade-offs between algorithms for quadruped locomotion, and quantify the energy costs of different learning approaches for computer chip-design. In addition, we propose a data cost metric to account for the cost incurred acquiring offline data for imitation learning and hybrid algorithms, which allows us to better compare these approaches. A2Perf also contains several standard baselines, enabling apples-to-apples comparisons across methods and facilitating progress in real-world autonomy. As an open-source benchmark, A2Perf is designed to remain accessible, up-to-date, and useful to the research community over the long term.
WarmFed: Federated Learning with Warm-Start for Globalization and Personalization Via Personalized Diffusion Models
Authors: Tao Feng, Jie Zhang, Xiangjian Li, Rong Huang, Huashan Liu, Zhijie Wang
Date: 2025-03-05 00:00:00
Tasks: Federated Learning, Personalized Federated Learning

Federated Learning (FL) stands as a prominent distributed learning paradigm among multiple clients to achieve a unified global model without privacy leakage. In contrast to FL, Personalized federated learning aims at serving for each client in achieving persoanlized model. However, previous FL frameworks have grappled with a dilemma: the choice between developing a singular global model at the server to bolster globalization or nurturing personalized model at the client to accommodate personalization. Instead of making trade-offs, this paper commences its discourse from the pre-trained initialization, obtaining resilient global information and facilitating the development of both global and personalized models. Specifically, we propose a novel method called WarmFed to achieve this. WarmFed customizes Warm-start through personalized diffusion models, which are generated by local efficient fine-tunining (LoRA). Building upon the Warm-Start, we advance a server-side fine-tuning strategy to derive the global model, and propose a dynamic self-distillation (DSD) to procure more resilient personalized models simultaneously. Comprehensive experiments underscore the substantial gains of our approach across both global and personalized models, achieved within just one-shot and five communication(s).
Predicting Space Tourism Demand Using Explainable AI
Authors: Tan-Hanh Pham, Jingchen Bi, Rodrigo Mesa-Arangom, Kim-Doang Nguyen
Date: 2025-03-05 00:00:00
Tasks: Demand Forecasting, Marketing

Comprehensive forecasts of space tourism demand are crucial for businesses to optimize strategies and customer experiences in this burgeoning industry. Traditional methods struggle to capture the complex factors influencing an individual's decision to travel to space. In this paper, we propose an explainable and trustworthy artificial intelligence framework to address the challenge of predicting space tourism demand by following the National Institute of Standards and Technology guidelines. We develop a novel machine learning network, called SpaceNet, capable of learning wide-range dependencies in data and allowing us to analyze the relationships between various factors such as age, income, and risk tolerance. We investigate space travel demand in the US, categorizing it into four types: no travel, moon travel, suborbital, and orbital travel. To this end, we collected 1860 data points in many states and cities with different ages and then conducted our experiment with the data. From our experiments, the SpaceNet achieves an average ROC-AUC of 0.82 $\pm$ 0.088, indicating strong classification performance. Our investigation demonstrated that travel price, age, annual income, gender, and fatality probability are important features in deciding whether a person wants to travel or not. Beyond demand forecasting, we use explainable AI to provide interpretation for the travel-type decisions of an individual, offering insights into the factors driving interest in space travel, which is not possible with traditional classification methods. This knowledge enables businesses to tailor marketing strategies and optimize service offerings in this rapidly evolving market. To the best of our knowledge, this is the first work to implement an explainable and interpretable AI framework for investigating the factors influencing space tourism.
Exploring Neural Ordinary Differential Equations as Interpretable Healthcare classifiers
Authors: Shi Li
Date: 2025-03-05 00:00:00
Tasks: Decision Making, Representation Learning

Deep Learning has emerged as one of the most significant innovations in machine learning. However, a notable limitation of this field lies in the ``black box" decision-making processes, which have led to skepticism within groups like healthcare and scientific communities regarding its applicability. In response, this study introduces a interpretable approach using Neural Ordinary Differential Equations (NODEs), a category of neural network models that exploit the dynamics of differential equations for representation learning. Leveraging their foundation in differential equations, we illustrate the capability of these models to continuously process textual data, marking the first such model of its kind, and thereby proposing a promising direction for future research in this domain. The primary objective of this research is to propose a novel architecture for groups like healthcare that require the predictive capabilities of deep learning while emphasizing the importance of model transparency demonstrated in NODEs.
Bridging Molecular Graphs and Large Language Models
Authors: Runze Wang, Mingqi Yang, Yanming Shen
Date: 2025-03-05 00:00:00
Tasks: Few-Shot Learning

While Large Language Models (LLMs) have shown exceptional generalization capabilities, their ability to process graph data, such as molecular structures, remains limited. To bridge this gap, this paper proposes Graph2Token, an efficient solution that aligns graph tokens to LLM tokens. The key idea is to represent a graph token with the LLM token vocabulary, without fine-tuning the LLM backbone. To achieve this goal, we first construct a molecule-text paired dataset from multisources, including CHEBI and HMDB, to train a graph structure encoder, which reduces the distance between graphs and texts representations in the feature space. Then, we propose a novel alignment strategy that associates a graph token with LLM tokens. To further unleash the potential of LLMs, we collect molecular IUPAC name identifiers, which are incorporated into the LLM prompts. By aligning molecular graphs as special tokens, we can activate LLM generalization ability to molecular few-shot learning. Extensive experiments on molecular classification and regression tasks demonstrate the effectiveness of our proposed Graph2Token.
A Survey of Foundation Models for Environmental Science
Authors: Runlong Yu, Shengyu Chen, Yiqun Xie, Xiaowei Jia
Date: 2025-03-05 00:00:00
Tasks: Decision Making, Management, Survey

Modeling environmental ecosystems is essential for effective resource management, sustainable development, and understanding complex ecological processes. However, traditional methods frequently struggle with the inherent complexity, interconnectedness, and limited data of such systems. Foundation models, with their large-scale pre-training and universal representations, offer transformative opportunities by integrating diverse data sources, capturing spatiotemporal dependencies, and adapting to a broad range of tasks. This survey presents a comprehensive overview of foundation model applications in environmental science, highlighting advancements in forward prediction, data generation, data assimilation, downscaling, model ensembling, and decision-making across domains. We also detail the development process of these models, covering data collection, architecture design, training, tuning, and evaluation. By showcasing these emerging methods, we aim to foster interdisciplinary collaboration and advance the integration of cutting-edge machine learning for sustainable solutions in environmental science.
Online Bidding under RoS Constraints without Knowing the Value
Authors: Sushant Vijayan, Zhe Feng, Swati Padmanabhan, Karthikeyan Shanmugam, Arun Suggala, Di Wang
Date: 2025-03-05 00:00:00

We consider the problem of bidding in online advertising, where an advertiser aims to maximize value while adhering to budget and Return-on-Spend (RoS) constraints. Unlike prior work that assumes knowledge of the value generated by winning each impression ({e.g.,} conversions), we address the more realistic setting where the advertiser must simultaneously learn the optimal bidding strategy and the value of each impression opportunity. This introduces a challenging exploration-exploitation dilemma: the advertiser must balance exploring different bids to estimate impression values with exploiting current knowledge to bid effectively. To address this, we propose a novel Upper Confidence Bound (UCB)-style algorithm that carefully manages this trade-off. Via a rigorous theoretical analysis, we prove that our algorithm achieves $\widetilde{O}(\sqrt{T\log(|\mathcal{B}|T)})$ regret and constraint violation, where $T$ is the number of bidding rounds and $\mathcal{B}$ is the domain of possible bids. This establishes the first optimal regret and constraint violation bounds for bidding in the online setting with unknown impression values. Moreover, our algorithm is computationally efficient and simple to implement. We validate our theoretical findings through experiments on synthetic data, demonstrating that our algorithm exhibits strong empirical performance compared to existing approaches.