pwc-archive/papers-with-abstracts huggingface dataset:
Memory-Efficient Orthogonal Fine-Tuning with Principal Subspace Adaptation
Authors: Fei Wu, Jia Hu, Geyong Min, Shiqiang Wang
Date: 2025-05-16 00:00:00
Tasks: parameter-efficient fine-tuning

Driven by the relentless growth in model parameters, which renders full fine-tuning prohibitively expensive for large-scale deployment, parameter-efficient fine-tuning (PEFT) has emerged as a crucial approach for rapidly adapting large models to a wide range of downstream tasks. Among the PEFT family, orthogonal fine-tuning and its variants have demonstrated remarkable performance by preserving hyperspherical energy, which encodes pairwise angular similarity between neurons. However, these methods are inherently memory-inefficient due to the need to store intermediate activations from multiple full-dimensional sparse matrices. To address this limitation, we propose Memory-efficient Orthogonal Fine-Tuning (MOFT) with principal subspace adaptation. Specifically, we first establish a theoretical condition under which orthogonal transformations within a low-rank subspace preserve hyperspherical energy. Based on this insight, we constrain orthogonal fine-tuning to the principal subspace defined by the top-r components obtained through singular value decomposition and impose an additional constraint on the projection matrix to satisfy the preservation condition. To enhance MOFT's flexibility across tasks, we relax strict orthogonality by introducing two learnable scaling vectors. Extensive experiments on 37 diverse tasks and four models across NLP and CV demonstrate that MOFT consistently outperforms key baselines while significantly reducing the memory footprint of orthogonal fine-tuning.
Massive-STEPS: Massive Semantic Trajectories for Understanding POI Check-ins -- Dataset and Benchmarks
Authors: Wilson Wongso, Hao Xue, Flora D. Salim
Date: 2025-05-16 00:00:00
Tasks: Benchmarking

Understanding human mobility through Point-of-Interest (POI) recommendation is increasingly important for applications such as urban planning, personalized services, and generative agent simulation. However, progress in this field is hindered by two key challenges: the over-reliance on older datasets from 2012-2013 and the lack of reproducible, city-level check-in datasets that reflect diverse global regions. To address these gaps, we present Massive-STEPS (Massive Semantic Trajectories for Understanding POI Check-ins), a large-scale, publicly available benchmark dataset built upon the Semantic Trails dataset and enriched with semantic POI metadata. Massive-STEPS spans 12 geographically and culturally diverse cities and features more recent (2017-2018) and longer-duration (24 months) check-in data than prior datasets. We benchmarked a wide range of POI recommendation models on Massive-STEPS using both supervised and zero-shot approaches, and evaluated their performance across multiple urban contexts. By releasing Massive-STEPS, we aim to facilitate reproducible and equitable research in human mobility and POI recommendation. The dataset and benchmarking code are available at: https://github.com/cruiseresearchgroup/Massive-STEPS
A Set-Sequence Model for Time Series
Authors: Elliot L. Epstein, Apaar Sadhwani, Kay Giesecke
Date: 2025-05-16 00:00:00
Tasks: model, Time Series

In many financial prediction problems, the behavior of individual units (such as loans, bonds, or stocks) is influenced by observable unit-level factors and macroeconomic variables, as well as by latent cross-sectional effects. Traditional approaches attempt to capture these latent effects via handcrafted summary features. We propose a Set-Sequence model that eliminates the need for handcrafted features. The Set model first learns a shared cross-sectional summary at each period. The Sequence model then ingests the summary-augmented time series for each unit independently to predict its outcome. Both components are learned jointly over arbitrary sets sampled during training. Our approach harnesses the set nature of the cross-section and is computationally efficient, generating set summaries in linear time relative to the number of units. It is also flexible, allowing the use of existing sequence models and accommodating a variable number of units at inference. Empirical evaluations demonstrate that our Set-Sequence model significantly outperforms benchmarks on stock return prediction and mortgage behavior tasks. Code will be released.
Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline
Authors: Xvyuan Liu, Xiangfei Qiu, Xingjian Wu, Zhengyu Li, Chenjuan Guo, Jilin Hu, Bin Yang
Date: 2025-05-16 00:00:00
Tasks: Astronomy, Irregular Time Series, Time Series, Time Series Forecasting

The forecasting of irregular multivariate time series (IMTS) is crucial in key areas such as healthcare, biomechanics, climate science, and astronomy. However, achieving accurate and practical predictions is challenging due to two main factors. First, the inherent irregularity and data missingness in irregular time series make modeling difficult. Second, most existing methods are typically complex and resource-intensive. In this study, we propose a general framework called APN to address these challenges. Specifically, we design a novel Time-Aware Patch Aggregation (TAPA) module that achieves adaptive patching. By learning dynamically adjustable patch boundaries and a time-aware weighted averaging strategy, TAPA transforms the original irregular sequences into high-quality, regularized representations in a channel-independent manner. Additionally, we use a simple query module to effectively integrate historical information while maintaining the model's efficiency. Finally, predictions are made by a shallow MLP. Experimental results on multiple real-world datasets show that APN outperforms existing state-of-the-art methods in both efficiency and accuracy.
Delta Attention: Fast and Accurate Sparse Attention Inference by Delta Correction
Authors: Jeffrey Willette, Heejun Lee, Sung Ju Hwang
Date: 2025-05-16 00:00:00

The attention mechanism of a transformer has a quadratic complexity, leading to high inference costs and latency for long sequences. However, attention matrices are mostly sparse, which implies that many entries may be omitted from computation for efficient inference. Sparse attention inference methods aim to reduce this computational burden; however, they also come with a troublesome performance degradation. We discover that one reason for this degradation is that the sparse calculation induces a distributional shift in the attention outputs. The distributional shift causes decoding-time queries to fail to align well with the appropriate keys from the prefill stage, leading to a drop in performance. We propose a simple, novel, and effective procedure for correcting this distributional shift, bringing the distribution of sparse attention outputs closer to that of quadratic attention. Our method can be applied on top of any sparse attention method, and results in an average 36%pt performance increase, recovering 88% of quadratic attention accuracy on the 131K RULER benchmark when applied on top of sliding window attention with sink tokens while only adding a small overhead. Our method can maintain approximately 98.5% sparsity over full quadratic attention, making our model 32 times faster than Flash Attention 2 when processing 1M token prefills.
Fourier Low-rank and Sparse Tensor for Efficient Tensor Completion
Authors: Jingyang Li, Jiuqian Shang, Yang Chen
Date: 2025-05-16 00:00:00
Tasks: Computational Efficiency

Tensor completion is crucial in many scientific domains with missing data problems. Traditional low-rank tensor models, including CP, Tucker, and Tensor-Train, exploit low-dimensional structures to recover missing data. However, these methods often treat all tensor modes symmetrically, failing to capture the unique spatiotemporal patterns inherent in scientific data, where the temporal component exhibits both low-frequency stability and high-frequency variations. To address this, we propose a novel model, \underline{F}ourier \underline{Lo}w-rank and \underline{S}parse \underline{T}ensor (FLoST), which decomposes the tensor along the temporal dimension using a Fourier transform. This approach captures low-frequency components with low-rank matrices and high-frequency fluctuations with sparsity, resulting in a hybrid structure that efficiently models both smooth and localized variations. Compared to the well-known tubal-rank model, which assumes low-rankness across all frequency components, FLoST requires significantly fewer parameters, making it computationally more efficient, particularly when the time dimension is large. Through theoretical analysis and empirical experiments, we demonstrate that FLoST outperforms existing tensor completion models in terms of both accuracy and computational efficiency, offering a more interpretable solution for spatiotemporal data reconstruction.
Driving Mechanisms and Forecasting of China's Pet Population-An ARIMA-RF-HW Hybrid Approach
Authors: Shengjia Chang, Xianshuo Yue
Date: 2025-05-16 00:00:00
Tasks: Imputation, Management

This study proposes a dynamically weighted ARIMA-RF-HW hybrid model integrating ARIMA for seasonality and trends, Random Forest for nonlinear features, and Holt-Winters smoothing for seasonal adjustment to improve China's pet population forecasting accuracy. Using 2005-2023 data with nine economic, social, and policy indicators (urban income, consumption, aging ratio, policy quantity, new veterinary drug approvals), data were preprocessed via Z-score normalization and missing value imputation. The results show that key drivers of pet populations include urban income (19.48% for cats, 17.15% for dogs), consumption (17.99% for cats), and policy quantity (13.33% for cats, 14.02% for dogs), with aging (12.81% for cats, 13.27% for dogs) and urbanization amplifying the demand for pets. Forecasts show steady cat growth and fluctuating dog numbers, reflecting cats' adaptability to urban environments. This research supports policymakers in optimizing pet health management and guides enterprises in developing differentiated services, advancing sustainable industry growth.
Multiclass threshold-based classification
Authors: Francesco Marchetti, Edoardo Legnaro, Sabrina Guastavino
Date: 2025-05-16 00:00:00
Tasks: Classification

In this paper, we introduce a threshold-based framework for multiclass classification that generalizes the standard argmax rule. This is done by replacing the probabilistic interpretation of softmax outputs with a geometric one on the multidimensional simplex, where the classification depends on a multidimensional threshold. This change of perspective enables for any trained classification network an a posteriori optimization of the classification score by means of threshold tuning, as usually carried out in the binary setting. This allows a further refinement of the prediction capability of any network. Moreover, this multidimensional threshold-based setting makes it possible to define score-oriented losses, which are based on the interpretation of the threshold as a random variable. Our experiments show that the multidimensional threshold tuning yields consistent performance improvements across various networks and datasets, and that the proposed multiclass score-oriented losses are competitive with standard loss functions, resembling the advantages observed in the binary case.
SubROC: AUC-Based Discovery of Exceptional Subgroup Performance for Binary Classifiers
Authors: Tom Siegl, Kutalmış Coşkun, Bjarne Hiller, Amin Mirzaei, Florian Lemmerich, Martin Becker
Date: 2025-05-16 00:00:00

Machine learning (ML) is increasingly employed in real-world applications like medicine or economics, thus, potentially affecting large populations. However, ML models often do not perform homogeneously across such populations resulting in subgroups of the population (e.g., sex=female AND marital_status=married) where the model underperforms or, conversely, is particularly accurate. Identifying and describing such subgroups can support practical decisions on which subpopulation a model is safe to deploy or where more training data is required. The potential of identifying and analyzing such subgroups has been recognized, however, an efficient and coherent framework for effective search is missing. Consequently, we introduce SubROC, an open-source, easy-to-use framework based on Exceptional Model Mining for reliably and efficiently finding strengths and weaknesses of classification models in the form of interpretable population subgroups. SubROC incorporates common evaluation measures (ROC and PR AUC), efficient search space pruning for fast exhaustive subgroup search, control for class imbalance, adjustment for redundant patterns, and significance testing. We illustrate the practical benefits of SubROC in case studies as well as in comparative analyses across multiple datasets.
Bidirectional Information Flow (BIF) -- A Sample Efficient Hierarchical Gaussian Process for Bayesian Optimization
Authors: Juan D. Guerra, Thomas Garbay, Guillaume Lajoie, Marco Bonizzato
Date: 2025-05-16 00:00:00
Tasks: Bayesian Optimization

Hierarchical Gaussian Process (H-GP) models divide problems into different subtasks, allowing for different models to address each part, making them well-suited for problems with inherent hierarchical structure. However, typical H-GP models do not fully take advantage of this structure, only sending information up or down the hierarchy. This one-way coupling limits sample efficiency and slows convergence. We propose Bidirectional Information Flow (BIF), an efficient H-GP framework that establishes bidirectional information exchange between parent and child models in H-GPs for online training. BIF retains the modular structure of hierarchical models - the parent combines subtask knowledge from children GPs - while introducing top-down feedback to continually refine children models during online learning. This mutual exchange improves sample efficiency, enables robust training, and allows modular reuse of learned subtask models. BIF outperforms conventional H-GP Bayesian Optimization methods, achieving up to 85% and 5x higher $R^2$ scores for the parent and children respectively, on synthetic and real-world neurostimulation optimization tasks.
Graph Representational Learning: When Does More Expressivity Hurt Generalization?
Authors: Sohir Maskey, Raffaele Paolino, Fabian Jogl, Gitta Kutyniok, Johannes F. Lutzeyer
Date: 2025-05-16 00:00:00
Tasks: Generalization Bounds

Graph Neural Networks (GNNs) are powerful tools for learning on structured data, yet the relationship between their expressivity and predictive performance remains unclear. We introduce a family of premetrics that capture different degrees of structural similarity between graphs and relate these similarities to generalization, and consequently, the performance of expressive GNNs. By considering a setting where graph labels are correlated with structural features, we derive generalization bounds that depend on the distance between training and test graphs, model complexity, and training set size. These bounds reveal that more expressive GNNs may generalize worse unless their increased complexity is balanced by a sufficiently large training set or reduced distance between training and test graphs. Our findings relate expressivity and generalization, offering theoretical insights supported by empirical results.
Heterogeneity-Aware Client Sampling: A Unified Solution for Consistent Federated Learning
Authors: Shudi Weng, Chao Ren, Ming Xiao, Mikael Skoglund
Date: 2025-05-16 00:00:00
Tasks: Federated Learning

Federated learning (FL) commonly involves clients with diverse communication and computational capabilities. Such heterogeneity can significantly distort the optimization dynamics and lead to objective inconsistency, where the global model converges to an incorrect stationary point potentially far from the pursued optimum. Despite its critical impact, the joint effect of communication and computation heterogeneity has remained largely unexplored, due to the intrinsic complexity of their interaction. In this paper, we reveal the fundamentally distinct mechanisms through which heterogeneous communication and computation drive inconsistency in FL. To the best of our knowledge, this is the first unified theoretical analysis of general heterogeneous FL, offering a principled understanding of how these two forms of heterogeneity jointly distort the optimization trajectory under arbitrary choices of local solvers. Motivated by these insights, we propose Federated Heterogeneity-Aware Client Sampling, FedACS, a universal method to eliminate all types of objective inconsistency. We theoretically prove that FedACS converges to the correct optimum at a rate of $O(1/\sqrt{R})$, even in dynamic heterogeneous environments. Extensive experiments across multiple datasets show that FedACS outperforms state-of-the-art and category-specific baselines by 4.3%-36%, while reducing communication costs by 22%-89% and computation loads by 14%-105%, respectively.
Effective Probabilistic Time Series Forecasting with Fourier Adaptive Noise-Separated Diffusion
Authors: Xinyan Wang, Rui Dai, Kaikui Liu, Xiangxiang Chu
Date: 2025-05-16 00:00:00
Tasks: Computational Efficiency, Denoising, Probabilistic Time Series Forecasting, regression, Time Series, Time Series Forecasting

We propose the Fourier Adaptive Lite Diffusion Architecture (FALDA), a novel probabilistic framework for time series forecasting. First, we introduce the Diffusion Model for Residual Regression (DMRR) framework, which unifies diffusion-based probabilistic regression methods. Within this framework, FALDA leverages Fourier-based decomposition to incorporate a component-specific architecture, enabling tailored modeling of individual temporal components. A conditional diffusion model is utilized to estimate the future noise term, while our proposed lightweight denoiser, DEMA (Decomposition MLP with AdaLN), conditions on the historical noise term to enhance denoising performance. Through mathematical analysis and empirical validation, we demonstrate that FALDA effectively reduces epistemic uncertainty, allowing probabilistic learning to primarily focus on aleatoric uncertainty. Experiments on six real-world benchmarks demonstrate that FALDA consistently outperforms existing probabilistic forecasting approaches across most datasets for long-term time series forecasting while achieving enhanced computational efficiency without compromising accuracy. Notably, FALDA also achieves superior overall performance compared to state-of-the-art (SOTA) point forecasting approaches, with improvements of up to 9%.
Diffusion Learning with Partial Agent Participation and Local Updates
Authors: Elsa Rizk, Kun Yuan, Ali H. Sayed
Date: 2025-05-16 00:00:00

Diffusion learning is a framework that endows edge devices with advanced intelligence. By processing and analyzing data locally and allowing each agent to communicate with its immediate neighbors, diffusion effectively protects the privacy of edge devices, enables real-time response, and reduces reliance on central servers. However, traditional diffusion learning relies on communication at every iteration, leading to communication overhead, especially with large learning models. Furthermore, the inherent volatility of edge devices, stemming from power outages or signal loss, poses challenges to reliable communication between neighboring agents. To mitigate these issues, this paper investigates an enhanced diffusion learning approach incorporating local updates and partial agent participation. Local updates will curtail communication frequency, while partial agent participation will allow for the inclusion of agents based on their availability. We prove that the resulting algorithm is stable in the mean-square error sense and provide a tight analysis of its Mean-Square-Deviation (MSD) performance. Various numerical experiments are conducted to illustrate our theoretical findings.
Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data
Authors: Lothar Heimbach, Sebastian Kaltenbach, Petr Karnakov, Francis J. Alexander, Petros Koumoutsakos
Date: 2025-05-16 00:00:00

Partial Differential Equations (PDEs) describe phenomena ranging from turbulence and epidemics to quantum mechanics and financial markets. Despite recent advances in computational science, solving such PDEs for real-world applications remains prohibitively expensive because of the necessity of resolving a broad range of spatiotemporal scales. In turn, practitioners often rely on coarse-grained approximations of the original PDEs, trading off accuracy for reduced computational resources. To mitigate the loss of detail inherent in such approximations, closure models are employed to represent unresolved spatiotemporal interactions. We present a framework for developing closure models for PDEs using synthetic data acquired through the method of manufactured solutions. These data are used in conjunction with reinforcement learning to provide closures for coarse-grained PDEs. We illustrate the efficacy of our method using the one-dimensional and two-dimensional Burgers' equations and the two-dimensional advection equation. Moreover, we demonstrate that closure models trained for inhomogeneous PDEs can be effectively generalized to homogeneous PDEs. The results demonstrate the potential for developing accurate and computationally efficient closure models for systems with scarce data.
Where You Place the Norm Matters: From Prejudiced to Neutral Initializations
Authors: Emanuele Francazi, Francesco Pinto, Aurelien Lucchi, Marco Baity-Jesi
Date: 2025-05-16 00:00:00

Normalization layers, such as Batch Normalization and Layer Normalization, are central components in modern neural networks, widely adopted to improve training stability and generalization. While their practical effectiveness is well documented, a detailed theoretical understanding of how normalization affects model behavior, starting from initialization, remains an important open question. In this work, we investigate how both the presence and placement of normalization within hidden layers influence the statistical properties of network predictions before training begins. In particular, we study how these choices shape the distribution of class predictions at initialization, which can range from unbiased (Neutral) to highly concentrated (Prejudiced) toward a subset of classes. Our analysis shows that normalization placement induces systematic differences in the initial prediction behavior of neural networks, which in turn shape the dynamics of learning. By linking architectural choices to prediction statistics at initialization, our work provides a principled understanding of how normalization can influence early training behavior and offers guidance for more controlled and interpretable network design.