pwc-archive/papers-with-abstracts huggingface dataset:
PVBF: A Framework for Mitigating Parameter Variation Imbalance in Online Continual Learning
Authors: Zelin Tao, Hao Deng, Mingqing Liu, Lijun Zhang, Shengjie Zhao
Date: 2025-02-25 00:00:00
Tasks: Continual Learning

Online continual learning (OCL), which enables AI systems to adaptively learn from non-stationary data streams, is commonly achieved using experience replay (ER)-based methods that retain knowledge by replaying stored past during training. However, these methods face challenges of prediction bias, stemming from deviations in parameter update directions during task transitions. This paper identifies parameter variation imbalance as a critical factor contributing to prediction bias in ER-based OCL. Specifically, using the proposed parameter variation evaluation method, we highlight two types of imbalance: correlation-induced imbalance, where certain parameters are disproportionately updated across tasks, and layer-wise imbalance, where output layer parameters update faster than those in preceding layers. To mitigate the above imbalances, we propose the Parameter Variation Balancing Framework (PVBF), which incorporates: 1) a novel method to compute parameter correlations with previous tasks based on parameter variations, 2) an encourage-and-consolidate (E&C) method utilizing parameter correlations to perform gradient adjustments across all parameters during training, 3) a dual-layer copy weights with reinit (D-CWR) strategy to slowly update output layer parameters for frequently occuring sample categories. Experiments on short and long task sequences demonstrate that PVBF significantly reduces prediction bias and improves OCL performance, achieving up to 47\% higher accuracy compared to existing ER-based methods.
C-LoRA: Continual Low-Rank Adaptation for Pre-trained Models
Authors: Xin Zhang, Liang Bai, Xian Yang, Jiye Liang
Date: 2025-02-25 00:00:00
Tasks: Continual Learning

Low-Rank Adaptation (LoRA) is an efficient fine-tuning method that has been extensively applied in areas such as natural language processing and computer vision. Existing LoRA fine-tuning approaches excel in static environments but struggle in dynamic learning due to reliance on multiple adapter modules, increasing overhead and complicating inference. We propose Continual Low-Rank Adaptation (C-LoRA), a novel extension of LoRA for continual learning. C-LoRA uses a learnable routing matrix to dynamically manage parameter updates across tasks, ensuring efficient reuse of learned subspaces while enforcing orthogonality to minimize interference and forgetting. Unlike existing approaches that require separate adapters for each task, C-LoRA enables a integrated approach for task adaptation, achieving both scalability and parameter efficiency in sequential learning scenarios. C-LoRA achieves state-of-the-art accuracy and parameter efficiency on benchmarks while providing theoretical insights into its routing matrix's role in retaining and transferring knowledge, establishing a scalable framework for continual learning.
Techniques for Enhancing Memory Capacity of Reservoir Computing
Authors: Atsuki Yokota, Ichiro Kawashima, Yohei Saito, Hakaru Tamukoh, Osamu Nomura, Takashi Morie
Date: 2025-02-25 00:00:00

Reservoir Computing (RC) is a bio-inspired machine learning framework, and various models have been proposed. RC is a well-suited model for time series data processing, but there is a trade-off between memory capacity and nonlinearity. In this study, we propose methods to improve the memory capacity of reservoir models by modifying their network configuration except for the inside of reservoirs. The Delay method retains past inputs by adding delay node chains to the input layer with the specified number of delay steps. To suppress the effect of input value increase due to the Delay method, we divide the input weights by the number of added delay steps. The Pass through method feeds input values directly to the output layer. The Clustering method divides the input and reservoir nodes into multiple parts and integrates them at the output layer. We applied these methods to an echo state network (ESN), a typical RC model, and the chaotic Boltzmann machine (CBM)-RC, which can be efficiently implemented in integrated circuits. We evaluated their performance on the NARMA task, and measured information processing capacity (IPC) to evaluate the trade-off between memory capacity and nonlinearity.
Model-Free Adversarial Purification via Coarse-To-Fine Tensor Network Representation
Authors: Guang Lin, Duc Thien Nguyen, Zerui Tao, Konstantinos Slavakis, Toshihisa Tanaka, Qibin Zhao
Date: 2025-02-25 00:00:00
Tasks: Adversarial Purification

Deep neural networks are known to be vulnerable to well-designed adversarial attacks. Although numerous defense strategies have been proposed, many are tailored to the specific attacks or tasks and often fail to generalize across diverse scenarios. In this paper, we propose Tensor Network Purification (TNP), a novel model-free adversarial purification method by a specially designed tensor network decomposition algorithm. TNP depends neither on the pre-trained generative model nor the specific dataset, resulting in strong robustness across diverse adversarial scenarios. To this end, the key challenge lies in relaxing Gaussian-noise assumptions of classical decompositions and accommodating the unknown distribution of adversarial perturbations. Unlike the low-rank representation of classical decompositions, TNP aims to reconstruct the unobserved clean examples from an adversarial example. Specifically, TNP leverages progressive downsampling and introduces a novel adversarial optimization objective to address the challenge of minimizing reconstruction error but without inadvertently restoring adversarial perturbations. Extensive experiments conducted on CIFAR-10, CIFAR-100, and ImageNet demonstrate that our method generalizes effectively across various norm threats, attack types, and tasks, providing a versatile and promising adversarial purification technique.
Machine Learning-Based Prediction of ICU Mortality in Sepsis-Associated Acute Kidney Injury Patients Using MIMIC-IV Database with Validation from eICU Database
Authors: Shuheng Chen, Junyi Fan, Elham Pishgar, Kamiar Alaei, Greg Placencia, Maryam Pishgar
Date: 2025-02-25 00:00:00
Tasks: ICU Mortality, Mortality Prediction

Background: Sepsis-Associated Acute Kidney Injury (SA-AKI) leads to high mortality in intensive care. This study develops machine learning models using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database to predict Intensive Care Unit (ICU) mortality in SA-AKI patients. External validation is conducted using the eICU Collaborative Research Database. Methods: For 9,474 identified SA-AKI patients in MIMIC-IV, key features like lab results, vital signs, and comorbidities were selected using Variance Inflation Factor (VIF), Recursive Feature Elimination (RFE), and expert input, narrowing to 24 predictive variables. An Extreme Gradient Boosting (XGBoost) model was built for in-hospital mortality prediction, with hyperparameters optimized using GridSearch. Model interpretability was enhanced with SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). External validation was conducted using the eICU database. Results: The proposed XGBoost model achieved an internal Area Under the Receiver Operating Characteristic curve (AUROC) of 0.878 (95% Confidence Interval: 0.859-0.897). SHAP identified Sequential Organ Failure Assessment (SOFA), serum lactate, and respiratory rate as key mortality predictors. LIME highlighted serum lactate, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, total urine output, and serum calcium as critical features. Conclusions: The integration of advanced techniques with the XGBoost algorithm yielded a highly accurate and interpretable model for predicting SA-AKI mortality across diverse populations. It supports early identification of high-risk patients, enhancing clinical decision-making in intensive care. Future work needs to focus on enhancing adaptability, versatility, and real-world applications.
Provable Performance Bounds for Digital Twin-driven Deep Reinforcement Learning in Wireless Networks: A Novel Digital-Twin Bisimulation Metric
Authors: Zhenyu Tao, Wei Xu, Xiaohu You
Date: 2025-02-25 00:00:00
Tasks: Deep Reinforcement Learning

Digital twin (DT)-driven deep reinforcement learning (DRL) has emerged as a promising paradigm for wireless network optimization, offering safe and efficient training environment for policy exploration. However, in theory existing methods cannot always guarantee real-world performance of DT-trained policies before actual deployment, due to the absence of a universal metric for assessing DT's ability to support reliable DRL training transferrable to physical networks. In this paper, we propose the DT bisimulation metric (DT-BSM), a novel metric based on the Wasserstein distance, to quantify the discrepancy between Markov decision processes (MDPs) in both the DT and the corresponding real-world wireless network environment. We prove that for any DT-trained policy, the sub-optimality of its performance (regret) in the real-world deployment is bounded by a weighted sum of the DT-BSM and its sub-optimality within the MDP in the DT. Then, a modified DT-BSM based on the total variation distance is also introduced to avoid the prohibitive calculation complexity of Wasserstein distance for large-scale wireless network scenarios. Further, to tackle the challenge of obtaining accurate transition probabilities of the MDP in real world for the DT-BSM calculation, we propose an empirical DT-BSM method based on statistical sampling. We prove that the empirical DT-BSM always converges to the desired theoretical one, and quantitatively establish the relationship between the required sample size and the target level of approximation accuracy. Numerical experiments validate this first theoretical finding on the provable and calculable performance bounds for DT-driven DRL.
Enhancing 5G O-RAN Communication Efficiency Through AI-Based Latency Forecasting
Authors: Raúl Parada, Ebrahim Abu-Helalah, Jordi Serra, Anton Aguilar, Paolo Dini
Date: 2025-02-25 00:00:00
Tasks: Management

The increasing complexity and dynamic nature of 5G open radio access networks (O-RAN) pose significant challenges to maintaining low latency, high throughput, and resource efficiency. While existing methods leverage machine learning for latency prediction and resource management, they often lack real-world scalability and hardware validation. This paper addresses these limitations by presenting an artificial intelligence-driven latency forecasting system integrated into a functional O-RAN prototype. The system uses a bidirectional long short-term memory model to predict latency in real time within a scalable, open-source framework built with FlexRIC. Experimental results demonstrate the model's efficacy, achieving a loss metric below 0.04, thus validating its applicability in dynamic 5G environments.
Stackelberg Game Preference Optimization for Data-Efficient Alignment of Language Models
Authors: Xu Chu, Zhixin Zhang, Tianyu Jia, Yujie Jin
Date: 2025-02-25 00:00:00
Tasks: 2k, Models Alignment

Aligning language models with human preferences is critical for real-world deployment, but existing methods often require large amounts of high-quality human annotations. Aiming at a data-efficient alignment method, we propose Stackelberg Game Preference Optimization (SGPO), a framework that models alignment as a two-player Stackelberg game, where a policy (leader) optimizes against a worst-case preference distribution (follower) within an $\epsilon$-Wasserstein ball, ensuring robustness to (self-)annotation noise and distribution shifts. SGPO guarantees $O(\epsilon)$-bounded regret, unlike Direct Preference Optimization (DPO), which suffers from linear regret growth in the distribution mismatch. We instantiate SGPO with the Stackelberg Self-Annotated Preference Optimization (SSAPO) algorithm, which iteratively self-annotates preferences and adversarially reweights synthetic annotated preferences. Using only 2K seed preferences, from the UltraFeedback dataset, i.e., 1/30 of human labels in the dataset, our method achieves 35.82% GPT-4 win-rate with Mistral-7B and 40.12% with Llama3-8B-Instruct within three rounds of SSAPO.
Graph Augmentation for Cross Graph Domain Generalization
Authors: Guanzi Chen, Jiying Zhang, Yang Li
Date: 2025-02-25 00:00:00
Tasks: Data Augmentation, Domain Generalization, Node Classification

Cross-graph node classification, utilizing the abundant labeled nodes from one graph to help classify unlabeled nodes in another graph, can be viewed as a domain generalization problem of graph neural networks (GNNs) due to the structure shift commonly appearing among various graphs. Nevertheless, current endeavors for cross-graph node classification mainly focus on model training. Data augmentation approaches, a simple and easy-to-implement domain generalization technique, remain under-explored. In this paper, we develop a new graph structure augmentation for the crossgraph domain generalization problem. Specifically, low-weight edgedropping is applied to remove potential noise edges that may hinder the generalization ability of GNNs, stimulating the GNNs to capture the essential invariant information underlying different structures. Meanwhile, clustering-based edge-adding is proposed to generate invariant structures based on the node features from the same distribution. Consequently, with these augmentation techniques, the GNNs can maintain the domain invariant structure information that can improve the generalization ability. The experiments on out-ofdistribution citation network datasets verify our method achieves state-of-the-art performance among conventional augmentations.
Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraints
Authors: Mihaela Cătălina Stoian, Eleonora Giunchiglia
Date: 2025-02-25 00:00:00
Tasks: Tabular Data Generation

Synthetic tabular data generation has traditionally been a challenging problem due to the high complexity of the underlying distributions that characterise this type of data. Despite recent advances in deep generative models (DGMs), existing methods often fail to produce realistic datapoints that are well-aligned with available background knowledge. In this paper, we address this limitation by introducing Disjunctive Refinement Layer (DRL), a novel layer designed to enforce the alignment of generated data with the background knowledge specified in user-defined constraints. DRL is the first method able to automatically make deep learning models inherently compliant with constraints as expressive as quantifier-free linear formulas, which can define non-convex and even disconnected spaces. Our experimental analysis shows that DRL not only guarantees constraint satisfaction but also improves efficacy in downstream tasks. Notably, when applied to DGMs that frequently violate constraints, DRL eliminates violations entirely. Further, it improves performance metrics by up to 21.4% in F1-score and 20.9% in Area Under the ROC Curve, thus demonstrating its practical impact on data generation.
Unveiling and Causalizing CoT: A Causal Pespective
Authors: Jiarun Fu, Lizhong Ding, Hao Li, Pengqi Li, Qiuning Wei, Xu Chen
Date: 2025-02-25 00:00:00

Although Chain-of-Thought (CoT) has achieved remarkable success in enhancing the reasoning ability of large language models (LLMs), the mechanism of CoT remains a ``black box''. Even if the correct answers can frequently be obtained, existing CoTs struggle to make the reasoning understandable to human. In this paper, we unveil and causalize CoT from a causal perspective to ensure both correctness and understandability of all reasoning steps (to the best of our knowledge, the first such). We model causality of CoT via structural causal models (SCM) to unveil the reasoning mechanism of CoT. To measure the causality of CoT, we define the CoT Average Causal Effect (CACE) to test the causal relations between steps. For those steps without causality (wrong or unintelligible steps), we design a role-playing causal query algorithm to causalize these steps, resulting a causalized CoT with all steps correct and understandable. Experimental results on both open-source and closed-source LLMs demonstrate that the causal errors commonly in steps are effectively corrected and the reasoning ability of LLMs is significantly improved.
Global-Decision-Focused Neural ODEs for Proactive Grid Resilience Management
Authors: Shuyi Chen, Ferdinando Fioretto, Feng Qiu, Shixiang Zhu
Date: 2025-02-25 00:00:00
Tasks: Decision Making, Management, Prediction

Extreme hazard events such as wildfires and hurricanes increasingly threaten power systems, causing widespread outages and disrupting critical services. Recently, predict-then-optimize approaches have gained traction in grid operations, where system functionality forecasts are first generated and then used as inputs for downstream decision-making. However, this two-stage method often results in a misalignment between prediction and optimization objectives, leading to suboptimal resource allocation. To address this, we propose predict-all-then-optimize-globally (PATOG), a framework that integrates outage prediction with globally optimized interventions. At its core, our global-decision-focused (GDF) neural ODE model captures outage dynamics while optimizing resilience strategies in a decision-aware manner. Unlike conventional methods, our approach ensures spatially and temporally coherent decision-making, improving both predictive accuracy and operational efficiency. Experiments on synthetic and real-world datasets demonstrate significant improvements in outage prediction consistency and grid resilience.
Pretraining Frequency Predicts Compositional Generalization of CLIP on Real-World Tasks
Authors: Thaddäus Wiedemer, Yash Sharma, Ameya Prabhu, Matthias Bethge, Wieland Brendel
Date: 2025-02-17 00:00:00

We investigate the success conditions for compositional generalization of CLIP models on real-world data through performance prediction. Prior work shows that CLIP requires exponentially more pretraining data for linear performance gains on individual concepts. This sample-inefficient scaling could be mitigated if CLIP systematically understood new inputs as compositions of learned components, allowing rare observation to be mapped to common concepts. To explore CLIP's compositional generalization ability, we filter retrieval corpora for samples with object combinations not present in the pretraining corpus. We show that CLIP's performance on these samples can be accurately predicted from the pretraining frequencies of individual objects. Our findings demonstrate that CLIP learns to disentangle objects observed in its pretraining data and can recompose them straightforwardly. Additionally, we are the first to show how this ability scales with pretraining data. For data curation in practice, our results suggest that balancing object occurrences improves generalization, which should benefit CLIP's efficiency and accuracy without scaling data volume.
Structural Alignment Improves Graph Test-Time Adaptation
Authors: Hans Hao-Hsun Hsu, Shikun Liu, Han Zhao, Pan Li
Date: 2025-02-25 00:00:00
Tasks: Fraud Detection, Test-time Adaptation

Graph-based learning has achieved remarkable success in domains ranging from recommendation to fraud detection and particle physics by effectively capturing underlying interaction patterns. However, it often struggles to generalize when distribution shifts occur, particularly those involving changes in network connectivity or interaction patterns. Existing approaches designed to mitigate such shifts typically require retraining with full access to source data, rendering them infeasible under strict computational or privacy constraints. To address this limitation, we propose a test-time structural alignment (TSA) algorithm for Graph Test-Time Adaptation (GTTA), a novel method that aligns graph structures during inference without revisiting the source domain. Built upon a theoretically grounded treatment of graph data distribution shifts, TSA integrates three key strategies: an uncertainty-aware neighborhood weighting that accommodates structure shifts, an adaptive balancing of self-node and neighborhood-aggregated representations driven by node representations' signal-to-noise ratio, and a decision boundary refinement that corrects remaining label and feature shifts. Extensive experiments on synthetic and real-world datasets demonstrate that TSA can consistently outperform both non-graph TTA methods and state-of-the-art GTTA baselines.
WebGames: Challenging General-Purpose Web-Browsing AI Agents
Authors: George Thomas, Alex J. Chan, Jikun Kang, Wenqi Wu, Filippos Christianos, Fraser Greenlee, Andy Toulis, Marvin Purtorab
Date: 2025-02-25 00:00:00

We introduce WebGames, a comprehensive benchmark suite designed to evaluate general-purpose web-browsing AI agents through a collection of 50+ interactive challenges. These challenges are specifically crafted to be straightforward for humans while systematically testing the limitations of current AI systems across fundamental browser interactions, advanced input processing, cognitive tasks, workflow automation, and interactive entertainment. Our framework eliminates external dependencies through a hermetic testing environment, ensuring reproducible evaluation with verifiable ground-truth solutions. We evaluate leading vision-language models including GPT-4o, Claude Computer-Use, Gemini-1.5-Pro, and Qwen2-VL against human performance. Results reveal a substantial capability gap, with the best AI system achieving only 43.1% success rate compared to human performance of 95.7%, highlighting fundamental limitations in current AI systems' ability to handle common web interaction patterns that humans find intuitive. The benchmark is publicly available at webgames.convergence.ai, offering a lightweight, client-side implementation that facilitates rapid evaluation cycles. Through its modular architecture and standardized challenge specifications, WebGames provides a robust foundation for measuring progress in development of more capable web-browsing agents.
Mechanistic PDE Networks for Discovery of Governing Equations
Authors: Adeel Pervez, Efstratios Gavves, Francesco Locatello
Date: 2025-02-25 00:00:00
Tasks: GPU

We present Mechanistic PDE Networks -- a model for discovery of governing partial differential equations from data. Mechanistic PDE Networks represent spatiotemporal data as space-time dependent linear partial differential equations in neural network hidden representations. The represented PDEs are then solved and decoded for specific tasks. The learned PDE representations naturally express the spatiotemporal dynamics in data in neural network hidden space, enabling increased power for dynamical modeling. Solving the PDE representations in a compute and memory-efficient way, however, is a significant challenge. We develop a native, GPU-capable, parallel, sparse, and differentiable multigrid solver specialized for linear partial differential equations that acts as a module in Mechanistic PDE Networks. Leveraging the PDE solver, we propose a discovery architecture that can discover nonlinear PDEs in complex settings while also being robust to noise. We validate PDE discovery on a number of PDEs, including reaction-diffusion and Navier-Stokes equations.