MLJ 2025

284 papers

A Calibration Test for Evaluating Set-Based Epistemic Uncertainty Representations Mira Jürgens, Thomas Mortier, Eyke Hüllermeier, Viktor Bengs, Willem Waegeman

A Comparison of Latent Space Modeling Techniques in a Plain-Vanilla Autoencoder Setting Fabian Kächele, Maximilian Coblenz, Oliver Grothe

A Contrastive Neural Disentanglement Approach for Query Performance Prediction Sara Salamat, Negar Arabzadeh, Shirin Seyedsalehi, Amin Bigdeli, Morteza Zihayat, Ebrahim Bagheri

A Model for Intelligible Interaction Between Agents That Predict and Explain A. Baskar, Ashwin Srinivasan, Michael Bain, Enrico W. Coiera

A New Adaptive Gradient Method with Gradient Decomposition Zhou Shao, Hang Zhou, Tong Lin

A New Filter for Deformation-Invariant Persistence Diagram Kaifeng Zhang, Hang Zhang, Kai Ming Ting, Tianrun Liang

A New Formulation of Lipschitz Constrained with Functional Gradient Learning for GANs Chang Wan, Ke Fan, Xinwei Sun, Yanwei Fu, Minglu Li, Yunliang Jiang, Zhonglong Zheng

A New Geometry-Aware Non-Euclidean Distance Metric Mehran Ghaziasgar, Hossein Mahvash Mohammadi, Peyman Adibi

A Robust Adversarial Ensemble with Causal (feature Interaction) Interpretations for Image Classification Chunheng Zhao, Pierluigi Pisu, Gurcan Comert, Negash Begashaw, Varghese Mathew Vaidyan, Nina Hubig

A Survey on Self-Supervised Learning for Non-Sequential Tabular Data Wei-Yao Wang, Wei-Wei Du, Derek Xu, Wei Wang, Wen-Chih Peng

A Survey on Self-Supervised Methods for Visual Representation Learning Tobias Uelwer, Jan Robine, Stefan Sylvius Wagner, Marc Höftmann, Eric Upschulte, Sebastian Konietzny, Maike Behrendt, Stefan Harmeling

A Unified View of Forward and Backward Losses for Learning from Weak Labels Daniel Bacaicoa-Barber, Jesús Cid-Sueiro

A Utility-Driven Approach to Instance-Based Transfer Learning for Relational Domains Cainã Figueiredo Pereira, Daniel S. Menasché, Gerson Zaverucha, Aline Paes, Valmir C. Barbosa

Accelerated Algorithms for Convex and Non-Convex Optimization on Manifolds Lizhen Lin, Bayan Saparbayeva, Michael Minyi Zhang, David B. Dunson

Achieving Collective Welfare in Multi-Agent Reinforcement Learning via Suggestion Sharing Yue Jin, Shuangqing Wei, Giovanni Montana

Adapting Performance Metrics for Ordinal Classification to Interval Scale: Length Matters Giulia Binotto, Rosario Delgado

Adaptive Adapter Routing for Long-Tailed Class-Incremental Learning Zhi-Hong Qi, Da-Wei Zhou, Yiran Yao, Han-Jia Ye, De-Chuan Zhan

Adaptive Collaborative Minority Oversampling for Multi-Class Imbalanced Classification Su-Yang Zheng, Chou-Yong Chen, Xiao-Xi Zhao, Zhong-Liang Zhang

Adaptive Differentiable Trees for Transparent Learning on Data Streams Kirsten Köbschall, Lisa Hartung, Stefan Kramer

Adaptive Optimization for Prediction with Missing Data Dimitris Bertsimas, Arthur Delarue, Jean Pauphilet

Adaptive Parameters Identification for Nonlinear Dynamics Using Deep Permutation Invariant Networks Mouad Elaarabi, Domenico Borzacchiello, Philippe Le Bot, Yves Le Guennec, Sebastien Comas-Cardona

Adaptive Transformer Modelling of Density Function for Nonparametric Survival Analysis Xin Zhang, Deval Mehta, Yanan Hu, Chao Zhu, David Darby, Zhen Yu, Daniel Merlo, Melissa Gresle, Anneke van der Walt, Helmut Butzkueven, Zongyuan Ge

Addressing Data Dependency in Neural Networks: Introducing the Knowledge Enhanced Neural Network (KENN) for Time Series Forecasting + Muhammad Ali Chattha, Muhammad Imran Malik, Andreas Dengel, Sheraz Ahmed

Addressing Task Conflicts in LLMs Multi-Task Fine-Tuning with Task-Specific Subnetwork Refinement Yiqun Wang, Chaoqun Wan, Xiang Tian, Xuesong Liu, Yaowu Chen

Alignclip: Navigating the Misalignments for Robust Vision-Language Generalization Zhongyi Han, Gongxu Luo, Hao Sun, Yaqian Li, Bo Han, Mingming Gong, Kun Zhang, Tongliang Liu

All-Time Safety and Sample-Efficient Meta Update for Online Safe Meta Reinforcement Learning Under Markov Task Transition Zhenyuan Yuan, Siyuan Xu, Minghui Zhu

ALM-PU: Positive and Unlabeled Learning with Constrained Optimization Jiazhe Wei, Yuefei Wu, Bin Shi, Ken Li, Bo Dong

An Efficient Bayes Error Rate Estimation Method Qingqiang Chen, Fuyuan Cao, Ying Xing, Jiye Liang

An Efficient Model Training Framework for Green AI Francesco Scala, Sergio Flesca, Luigi Pontieri

An Empirical Study on Impact of Label Noise on Synthetic Tabular Data Generation Jeonghoon Kim, Chao Huang, Xin Liu

An End-to-End Explainability Framework for Spatio-Temporal Predictive Modeling Massimiliano Altieri, Michelangelo Ceci, Roberto Corizzo

An In-Depth Review and Analysis of Mode Collapse in Generative Adversarial Networks Farhat Lamia Barsha, William Eberle

An Interpretable Sample Selection Framework Against Numerical Label Noise Gaoxia Jiang, Wenjian Wang

An Investigation of Visual Foundation Models Robustness Sandeep Gupta, Roberto Passerone

An Unsupervised Adversarial Domain Adaptation Based on Variational Auto-Encoder Mahta Hassan Pour Zonoozi, Vahid Seydi, Mahmood Deypir

Analyzing RL Components for Wagner's Framework via Brouwer's Conjecture Flora Angileri, Giulia Lombardi, Andrea Fois, Renato Faraone, Carlo Metta, Michele Salvi, Luigi Amedeo Bianchi, Marco Fantozzi, Silvia Giulia Galfrè, Daniele Pavesi, Maurizio Parton, Francesco Morandin

Analyzing the Effect of Residual Connections to Oversmoothing in Graph Neural Networks Dimitrios Kelesis, Dimitris Fotakis, Georgios Paliouras

ArithmeticGPT: Empowering Small-Size Large Language Models with Advanced Arithmetic Skills Zitao Liu, Ying Zheng, Zhibo Yin, Jiahao Chen, Tianqiao Liu, Mi Tian, Weiqi Luo

Assessing Machine Learning and Data Imputation Approaches to Handle the Issue of Data Sparsity in Sports Forecasting Fabian Wunderlich, Henrik Biermann, Weiran Yang, Manuel Bassek, Dominik Raabe, Nico Elbert, Daniel Memmert, Marc Garnica Caparrós

Assessing the Risk of Discriminatory Bias in Classification Datasets Kejun Dai, Jonathan Kim, Saso Dzeroski, Jörg Wicker, Gillian Dobbie, Katharina Dost

Auction-Based Incentive Mechanism with Personalized Privacy Protection in Federated Learning Siqin Zeng, Xiaohong Wu, Yonggen Gu, Jie Tao, Benfeng Chen, Guoqiang Li

Benchmarking Adversarial Robustness to Bias Elicitation in Large Language Models: Scalable Automated Assessment with LLM-as-a-Judge Riccardo Cantini, Alessio Orsino, Massimo Ruggiero, Domenico Talia

Beyond Limits: Enhancing the Extrapolation Performance of Regression Models by Leaving the Boundary Out Francisco Ambrosio Garcia, Frank Naets

Beyond What's Normal: Bimodal and Heaviside Alternatives to Gaussian Process Regression Thomas M. Whitehead

Birds Look like Cars: Adversarial Analysis of Intrinsically Interpretable Deep Learning Hubert Baniecki, Przemyslaw Biecek

Boolean Matrix Logic Programming for Active Learning of Gene Functions in Genome-Scale Metabolic Network Models Lun Ai, Stephen H. Muggleton, Shi-Shun Liang, Geoff S. Baldwin

Breaking Domain Barriers: Mixture of Experts for Cross-Domain Fake News Detection Angelica Liguori, Francesco Sergio Pisani, Carmela Comito, Massimo Guarascio, Giuseppe Manco

Bridging XAI and Spectral Analysis to Investigate the Inductive Biases of Deep Graph Networks Michele Fontanesi, Alessio Micheli, Marco Podda, Domenico Tortorella

CaCOM: Customizing Text-to-Image Diffusion Models in the Wild via Continual Active Selection Jianan Yang, Yanming Zhang, Haobo Wang, Gang Chen, Sai Wu, Junbo Zhao

Calibrated Explanations for Regression Tuwe Löfström, Helena Löfström, Ulf Johansson, Cecilia Sönströd, Rudy Matela

Capturing the Context-Aware Code Change via Dynamic Control Flow Graph for Commit Message Generation Yali Du, Ying Li, Yi-Fan Ma, Ming Li

Causal CSSE: Integrating Counterfactuals and Causality in the Explanation of Machine Learning Models Omar F. P. Krauss, Marcelo S. Balbino, Cristiane Neri Nobre

Causality from Bottom to Top: A Survey Abraham Itzhak Weinberg, Cristiano Premebida, Diego Resende Faria

Cherry on the Cake: Fairness Is NOT an Optimization Problem Marco Favier, Toon Calders

CLAIRE: Clustering Evaluation Based on Item Response Theory and Model Agreement Manuel Ferreira Junior, Eufrásio de Andrade Lima Neto, Marcelo Rodrigo Portela Ferreira, Telmo de Menezes e Silva Filho, Ricardo B. C. Prudêncio

Cluster Weighted Models for Functional Data Cristina Anton, Iain Nicholas Smith

Common Abductive Explanations in First Order Logic Céline Rouveirol, Henry Soldano, Malik Kazi Aoual, Véronique Ventos

Commonsense Knowledge Enhanced Event Graph Representation Learning for Script Event Prediction Xiang Li, Xinxi Jiang, Qifeng Zhou

Community Detection via Structured Adaptive Block-Diagonal Learning with Topology-Subspace Fusion Ling Wu, Ziqi Cai, Yingjie Yang, Kun Guo

Compression and Restoration: Exploring Elasticity in Continual Test-Time Adaptation Jingwei Li, Chengbao Liu, Xiwei Bai, Jie Tan, Jiaqi Chu, Yudong Wang

Computing the Distance Between Unbalanced Distributions: The Flat Metric Henri Schmidt, Christian Düll

Concentration Properties of Fractional Posterior in 1-Bit Matrix Completion The Tien Mai

Conformal Load Prediction with Transductive Graph Autoencoders Rui Luo, Nicolò Colombo

Conformal Multistep-Ahead Multivariate Time-Series Forecasting Filip Schlembach, Evgueni N. Smirnov, Irena Koprinska, Mark H. M. Winands

Conformalised Data Synthesis Julia A. Meister, Khuong An Nguyen

Constraint Guided Autoencoders for Joint Optimization of Condition Indicator Estimation and Anomaly Detection in Machine Condition Monitoring Maarten Meire, Quinten Van Baelen, Ted Ooijevaar, Peter Karsmakers

Construction of the Kolmogorov-Arnold Networks Using the Newton-Kaczmarz Method Michael Poluektov, Andrew Polar

Convergence of Adam for Non-Convex Objectives: Relaxed Hyperparameters and Non-Ergodic Case Yuqing Liang, Meixuan He, Jinlan Liu, Dongpo Xu

Cost-Sensitive Classification with Cost Uncertainty: Do We Need Surrogate Losses? Viacheslav Komisarenko, Meelis Kull

Counterfactual Ensembles for Interpretable Churn Prediction: From Real-World to Privacy-Preserving Synthetic Data Samuele Tonati, Marzio Di Vece, Fosca Giannotti, Roberto Pellungrini

CPAT: Cross-Patch Aggregated Transformer for Time Series Forecasting Bingyan Liu, Li Wu, Xiaoying Wang, Jianqiang Huang, Guojing Zhang

Credal Ensembling in Multi-Class Classification Vu-Linh Nguyen, Haifei Zhang, Sébastien Destercke

Data-Efficient Reinforcement Learning by Generalized Value Estimation Junjie Zhou, Ying Tian, Minglun Ren

DatRel: A Noise-Tolerant Data Relocation Approach for Effective Synthetic Data Generation in Imbalanced Classifiers Fatih Saglam

Ddog: Optimizing Multi-Hop Inference via Dual-Driven Retrieval and Reasoning Path Yan Chen, Bruce Gu, Longxiang Gao, Kexue Fu, Youyang Qu, Lei Cui

De-Biased Two-Sample U-Statistics with Application to Conditional Distribution Testing Yuchen Chen, Jing Lei

Deep Contrastive Coordinated Multi-View Consistency Clustering Fuhao Shi, Shaohua Wan, Shengli Wu, Hui Wei, Hu Lu

Deep Errors-in-Variables Using a Diffusion Model Josua Faller, Jörg Martin, Clemens Elster

Deep Latent Force Models: ODE-Based Process Convolutions for Bayesian Deep Learning Thomas Baldwin-McDonald, Xinxing Shi, Mingxin Shen, Mauricio A. Álvarez

Deep Learning of PDE Correction and Mesh Adaption Without Automatic Differentiation Shaocong Ma, James Diffenderfer, Bhavya Kailkhura, Yi Zhou

Deep Metric Loss for Multimodal Learning Sehwan Moon, Hyunju Lee

Deep Sub-Ensembles Meets Quantile Regression: Uncertainty-Aware Imputation for Time Series Ying Liu, Peng Cui, Wenbo Hu, Richang Hong

Developing Safe and Responsible Large Language Model: Can We Balance Bias Reduction and Language Understanding? Shaina Raza, Oluwanifemi Bamgbose, Shardul Ghuge, Fatemeh Tavakoli, Deepak John Reji, Syed Raza Bashir

Differentially-Private Data Synthetisation for Efficient Re-Identification Risk Control Tânia Carvalho, Nuno Moniz, Luis Antunes, Nitesh V. Chawla

Distribution Aligned Semantics Adaption for Lifelong Person Re-Identification Qizao Wang, Xuelin Qian, Bin Li, Xiangyang Xue

Distribution Estimation for Time Series via DNN-Based GANs with an Application to Change-Point Estimation Jianya Lu, Yingjun Mo, Zhijie Xiao, Lihu Xu, Qiuran Yao

DP-FedSecure: A Secure and Efficient Federated Learning Scheme Based on Adaptive Differential Privacy Shuo Chen, Tanping Zhou, Huiyu Xie, Weidong Du, Xiaoyuan Yang

Drop-in Efficient Self-Attention Approximation Method Damien François, Mathis Saillot, Jacques Klein, Tegawendé F. Bissyandé, Alexander Skupin

Dual-View Data Augmentation at Subgraph Level and Graph Contrastive Learning for Sequential Recommendation Caihong Mu, Haikun Yu, Lang Qin, Yi Liu

EchoRAG: A Framework for Enhancing Language Models with Graph-RAG and In-Context Learning William Jones Beckhauser, Renato Fileto

Efficient and Interpretable Raw Audio Classification with Diagonal State Space Models Matthias Bittner, Daniel Schnöll, Matthias Wess, Axel Jantsch

Efficient Federated Unlearning Under Plausible Deniability Ayush K. Varshney, Vicenç Torra

Efficient Projection-Free Online Convex Optimization Using Stochastic Gradients Jiahao Xie, Chao Zhang, Zebang Shen, Hui Qian

Efficient Quantification on Large-Scale Networks Alessio Micheli, Alejandro Moreo, Marco Podda, Fabrizio Sebastiani, William Simoni, Domenico Tortorella

Empirical Evaluation of Normalizing Flows in Markov Chain Monte Carlo David Nabergoj, Erik Strumbelj

Empirical Risk Minimization in the Interpolating Regime with Application to Neural Network Learning Nicole Mücke, Ingo Steinwart

Enhanced Route Planning with Calibrated Uncertainty Set Lingxuan Tang, Rui Luo, Zhixin Zhou, Nicolò Colombo

Enhancing Interpretability in Generative Modeling: Statistically Disentangled Latent Spaces Guided by Generative Factors in Scientific Datasets Arkaprabha Ganguli, Nesar Ramachandra, Julie Bessac, Emil M. Constantinescu

Enhancing Workforce Attendance Evaluation in Vocational Schools: A Decision Support Framework Powered by Explainable Machine Learning Joseph Teguh Santoso, Hendry Hendry, Daniel H. F. Manongga

Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data Frederik Pahde, Thomas Wiegand, Sebastian Lapuschkin, Wojciech Samek

Entangle-Then-Disentangle: A Novel Approach for Enhancing Large Vision-Language Model Jiajun Yuan, Haiting Zheng, Hang Yu, Xiangfeng Luo

Epidemic-Guided Deep Learning for Spatiotemporal Forecasting of Tuberculosis Outbreak Madhab Barman, Madhurima Panja, Nachiketa Mishra, Tanujit Chakraborty

Evaluation of Geographical Distortions in Language Models Rémy Decoupes, Roberto Interdonato, Mathieu Roche, Maguelonne Teisseire, Sarah Valentin

Evo-Path: A Two-Stage Temporal Knowledge Graph Reasoning Model and Its Application in Human Behavior Prediction Mingsheng He, Lin Zhu, Luyi Bai

Explaining Random Forest and XGBoost with Shallow Decision Trees by Co-Clustering Feature Importance Ruggero G. Pensa, Anton Crombach, Sergio Peignier, Christophe Rigotti

Explaining Word Embeddings with Perfect Fidelity: A Case Study in Predicting Research Impact Lucie Dvorackova, Marcin P. Joachimiak, Michal Cerny, Adriana Kubecova, Vilém Sklenák, Tomás Kliegr

Exploration and Generalization in Deep Learning with SwitchPath Activations Antonio Di Cecco, Andrea Papini, Carlo Metta, Marco Fantozzi, Silvia Giulia Galfrè, Francesco Morandin, Maurizio Parton

Fair Prediction Sets Through Multi-Objective Hyperparameter Optimization Alberto García-Galindo, Marcos Lopez-De-Castro, Rubén Armañanzas

Fast Online Feature Selection in Streaming Data Yael Hochma, Mark Last

Federated Causal Inference from Observational Data Thanh Vinh Vo, Young Lee, Tze-Yun Leong

Fedflow: A Personalized Federated Learning Framework for Passenger Flow Prediction Franca Rocco di Torrepadula, Marco Fisichella, Sergio Di Martino, Nicola Mazzocca

FediOS: Decoupling Orthogonal Subspaces for Personalization in Feature-Skew Federated Learning Lingzhi Gao, Zexi Li, Xinyi Shang, Yang Lu, Chao Wu

FLight: Optimization Algorithm for Traffic Lights Based on Short-Term Traffic State Forecast Daimin Liu, Jian Huang

Forecasting Short-Term Passenger Flow via CBGC-SCI: An In-Depth Comparative Study on Shenzhen Metro Yuxin He, Weihang Hong, Lishuai Li, Jinlei Zhang, Jin Qin, Qin Luo

Gender Disentangled Representation Learning in Neural Rankers Shirin Seyedsalehi, Sara Salamat, Negar Arabzadeh, Sajad Ebrahimi, Morteza Zihayat, Ebrahim Bagheri

Generalized Median of Means Principle for Bayesian Inference Stanislav Minsker, Shunan Yao

Gentle Local Robustness Implies Generalization Khoat Than, Dat Phan, Giang Vu

Gradient Descent Fails to Learn High-Frequency Functions and Modular Arithmetic Rustem Takhanov, Maxat Tezekbayev, Artur Pak, Arman Bolatov, Zhenisbek Assylbekov

Gradient-Based Causal Discovery with Latent Variables Haotian Ni, Tian-Zuo Wang, Hong Tao, Xiuqi Huang, Chenping Hou

Graph Spring Neural ODEs for Link Sign Prediction Andrin Rehmann, Alexandre Bovet

Grid-Mapping Pseudo-Count Constraint for Offline Reinforcement Learning Yi Shen, Hanyan Huang

Group Feature Selection Using Non-Class Data Chunna Li, Yuangang Pan, Weijie Chen, Ivor W. Tsang, Yuanhai Shao

HE-GAD: A Behavior-Enhanced Contrastive Learning Framework for Graph Anomaly Detection Ling Zheng, Qi Song, Yihan Wang, Zhitao Wang, Xiangyang Li

Heat Transfer Flow of Non-Newtonian Eyring-Powell Fluid with Mixed Convection Heterogeneous and Homogeneous Reactions Using Linear Regression Based Machine Learning Approach Rahmat Ellahi, N. Khalid, Ahmed Zeeshan, Sadiq M. Sait, Muhammad Ijaz Khan

HFIA: A Parasitic Feature Inference Attack and Gradient-Based Defense Strategy in SplitNN-Based Vertical Federated Learning Qixuan Dong, Boyang Zhou, ZhiQiang Ru, Ying He, Jingyu Hua, Sheng Zhong

Histogram Approaches for Imbalanced Data Streams Regression Ehsan Aminian, Rita P. Ribeiro, João Gama

HorNets: Learning from Discrete and Continuous Signals with Routing Neural Networks Boshko Koloski, Nada Lavrac, Blaz Skrlj

Hybrid Additive Modeling with Partial Dependence for Supervised Regression and Dynamical Systems Forecasting Yann Claes, Vân Anh Huynh-Thu, Pierre Geurts

Imbalanced Regression Pipeline Recommendation Juscimara Gomes Avelino, George D. C. Cavalcanti, Rafael M. O. Cruz

Improve Global Generalization for Personalized Federated Learning Within a Stackelberg Game Wei Xie, Runqun Xiong, Junzhou Luo

Improving Graph Neural Networks Through Feature Importance Learning Fouad Alkhoury, Tamás Horváth, Christian Bauckhage, Stefan Wrobel

Improving Text Processing via Adversarial Low-Rank Adaptation Hao Wu, Xiangfeng Luo, Jianqi Gao, Dian Huang

Inductive Biases for Zero-Shot Systematic Generalization in Language-Informed Reinforcement Learning Negin Hashemi Dijujin, Seyed Roozbeh Razavi Rohani, Mohammad Mahdi Samiei, Mahdieh Soleymani Baghshah

Inductive Learning of Robot Task Knowledge from Raw Data and Online Expert Feedback Daniele Meli, Paolo Fiorini

Inferring Individual Direct Causal Effects Under Heterogeneous Peer Influence Shishir Adhikari, Elena Zheleva

Integrating Human Knowledge for Explainable AI Eleonora Cappuccio, Bahavathy Kathirgamanathan, Salvatore Rinzivillo, Gennady L. Andrienko, Natalia V. Andrienko

Interactive Sequential Generative Models for Team Sports Dennis Fassmeyer, Moritz Cordes, Ulf Brefeld

Interpretable Deep Reinforcement Learning via Concept-Based Policy Distillation Yanzhe Bekkemoen, Helge Langseth

Interpretable Optimisation-Based Approach for Hyper-Box Classification Georgios I. Liapis, Sophia Tsoka, Lazaros G. Papageorgiou

Intramodal Consistency in Triplet-Based Cross-Modal Learning for Image Retrieval Mario Mallea, Ricardo Ñanculef, Mauricio Araya

Invariant Representation Learning via Decoupling Style and Spurious Features Ruimeng Li, Yuanhao Pu, Zhaoyi Li, Chenwang Wu, Hong Xie, Defu Lian

Is It Still Fair? a Comparative Evaluation of Fairness Algorithms Through the Lens of Covariate Drift Oscar Blessed Deho, Michael Bewong, Selasi Kwashie, Jiuyong Li, Jixue Liu, Lin Liu, Srecko Joksimovic

ISA3: A 3-Dimensional Expansion of Instance Space Analysis Connor Simpson, Mario Andrés Muñoz, Sevvandi Kandanaarachchi, Ricardo J. G. B. Campello

iSOUP-SymRF: Symbolic Feature Ranking with Random Forests in Online Multi-Target Regression and Multi-Label Classification Aljaz Osojnik, Pance Panov, Saso Dzeroski

JANET: Joint Adaptive predictioN-Region Estimation for Time-Series Eshant English, Eliot Wong-Toi, Matteo Fontana, Stephan Mandt, Padhraic Smyth, Christoph Lippert

Jensen-Tsallis Divergence for Supervised Classification Under Data Imbalance Antonio Squicciarini, Tom Trigano, David Luengo

Kernel Density Estimation for Multiclass Quantification Alejandro Moreo, Pablo González, Juan José del Coz

Knoop: Practical Enhancement of Knockoff with Over-Parameterization for Variable Selection Xiaochen Zhang, Yunfeng Cai, Haoyi Xiong

LEAP: Linear Equations for Classifier Accuracy Prediction Under Prior Probability Shift Lorenzo Volpi, Alejandro Moreo, Fabrizio Sebastiani

Learning Answer Set Programs with Aggregates via Sampling and Genetic Programming Damiano Azzolini

Learning Complementary Knowledge via Trusted Multi-View Space Decomposition for Self-Supervised Contrastive Learning Jiangmeng Li, Yunze Zhao, Yifan Jin, Changwen Zheng, Wenwen Qiang

Learning De-Biased Environment Models for Delivery Incentive Policy Optimization on Food Delivery Platforms Yu-Ren Liu, Xiong-Hui Chen, Siyuan Xiao, Xinyu Yang, Xintong Qi, Linjun Zhou, Yang Yu, Fangsheng Huang

Learning Global Object-Centric Representations via Disentangled Slot Attention Tonglin Chen, Yinxuan Huang, Zhimeng Shen, Jinghao Huang, Bin Li, Xiangyang Xue

Learning Green's Function Efficiently Using Low-Rank Approximations Kishan Wimalawarne, Taiji Suzuki, Sophie Langer

Learning Multi-Axis Representation in Frequency Domain for Medical Image Segmentation Jiacheng Ruan, Jingsheng Gao, Mingye Xie, Suncheng Xiang

Learning to Rank Aspects and Opinions for Comparative Explanations Trung-Hoang Le, Hady W. Lauw

Learning Using Statistical Invariants: When They Work and When They Don't Yang Liu, Ernest Fokoué, Daniel E. Krutz

Learning with Confidence: Training Better Classifiers from Soft Labels Sjoerd de Vries, Dirk Thierens

Least-Squares Temporal Difference with Expected Eligibility Traces Roy van Zuijlen, Duarte Antunes

Let the Noise Flow Away: Combating Noisy Labels Using Normalizing Flows Kuan-An Su, YiHao Su, Yun-Hsuan Lien, Yu-Shuen Wang

Leveraging Differentiable NAS and Abstract Genetic Algorithms for Optimizing On-Mobile VSR Performance Xuncheng Liu, Weizhan Zhang, Tieliang Gong, Caixia Yan, Rui Li

Likelihood-Ratio-Based Confidence Intervals for Neural Networks Laurens Sluijterman, Eric Cator, Tom Heskes

Linearly-Interpretable Concept Embedding Models for Text Analysis Francesco De Santis, Philippe Bich, Gabriele Ciravegna, Pietro Barbiero, Danilo Giordano, Tania Cerquitelli

LLiMe: Enhancing Text Classifier Explanations with Large Language Models Fabrizio Angiulli, Francesco De Luca, Fabio Fassetti, Simona Nisticò

LLM-Based Feature Generation from Text for Interpretable Machine Learning Vojtech Balek, Lukás Sýkora, Vilém Sklenák, Tomás Kliegr

Low-Resolution Human Pose Estimation and Action Recognition via Pose-Driven Super-Resolution Reconstruction Zhizhuo Zhang, Lili Wan, Wanru Xu, Shenghui Wang

Masked Autoencoder for Multiagent Trajectories Yannick Rudolph, Ulf Brefeld

MASS-CSP: Mining with Answer Set Solving for Contrast Sequential Pattern Mining Gioacchino Sterlicchio, Francesca Alessandra Lisi

Mast: Interpretable Stress Testing via Meta-Learning for Forecasting Model Robustness Evaluation Ricardo Inácio, Vítor Cerqueira, Marília Barandas, Carlos Soares

Maximum Causal Entropy Inverse Constrained Reinforcement Learning Mattijs Baert, Pietro Mazzaglia, Sam Leroux, Pieter Simoens

MbExplainer: Multilevel Bandit-Based Explanations for Downstream Models with Augmented Graph Embeddings Ashkan Golgoon, Ryan Franks, Khashayar Filom, Arjun Ravi Kannan

MetaML: A Multi-Label Meta-Learning Approach for Pipeline Recommendation Cynthia Moreira Maia, Lucas B. V. de Amorim, George D. C. Cavalcanti, Rafael M. O. Cruz

Mind the Gap: From Plausible to Valid Self-Explanations in Large Language Models Korbinian Randl, John Pavlopoulos, Aron Henriksson, Tony Lindgren

Minimal Learning Machine for Multi-Label Learning Joonas Hämäläinen, Antoine Hubermont, Amauri H. Souza, César L. C. Mattos, João P. P. Gomes, Tommi Kärkkäinen

Minimum Discrepancy Principle Strategy for Choosing K in k-NN Regression Yaroslav Averyanov, Alain Celisse

Mining Exceptional Social Behavior on Attributed Interaction Networks Martin Atzmueller, Carolina Centeio Jorge, Cláudio Rebelo de Sá, Behzad Momahed Heravi, Jenny L. Gibson, Rosaldo J. F. Rossetti

Misclassification Bounds for PAC-Bayesian Sparse Deep Learning The Tien Mai

Model-Driven Validation of Visual Explanations for Multimodal Emotion Recognition Guido Gagliardi, Antonio Luca Alfeo, Vincenzo Catrambone, Mario G. C. A. Cimino, Maarten De Vos, Gaetano Valenza

Modeling Spatio-Temporal Locality in Multi-Step Forecasting of Geo-Referenced Time Series Annunziata D'Aversa, Gianvito Pio, Michelangelo Ceci

Modelradar: Aspect-Based Forecast Evaluation Vítor Cerqueira, Luis Roque, Carlos Soares

Multi-Modal Co-Learning for Earth Observation: Enhancing Single-Modality Models via Modality Collaboration Francisco Alejandro Mena, Dino Ienco, Cássio Fraga Dantas, Roberto Interdonato, Andreas Dengel

Multivariate Functional Linear Discriminant Analysis for Partially-Observed Time Series Rahul Bordoloi, Clémence Réda, Orell Trautmann, Saptarshi Bej, Olaf Wolkenhauer

MUSO: Achieving Exact Machine Unlearning in Over-Parameterized Regimes Ruikai Yang, Mingzhen He, Zhenghao He, Youmei Qiu, Xiaolin Huang

Navigating Explanatory Multiverse Through Counterfactual Path Geometry Kacper Sokol, Edward Small, Yueqing Xuan

Nettop: A Light-Weight Network of Orthogonal-Plane Features for Image Recognition Thanh Tuan Nguyen, Thanh Phuong Nguyen

Neural RELAGGS Lukas Pensel, Stefan Kramer

Novel Applications of Item Response Theory for Analysing Data Set Complexity and Benchmark Selection João Luiz Junho Pereira, Alfredo Antonio Alencar Exposito de Queiroz, Telmo de Menezes e Silva Filho, Ana Carolina Lorena, Rafael Gomes Mantovani, Gisele Lobo Pappa, Ricardo Bastos Cavalcante Prudêncio

Offline Reinforcement Learning for Learning to Dispatch for Job Shop Scheduling Jesse van Remmerden, Zaharah Bukhsh, Yingqian Zhang

On the Convergence Analysis of Over-Parameterized Variational Autoencoders: A Neural Tangent Kernel Perspective Li Wang, Wei Huang

On the Usefulness of the Fit-on-Test View on Evaluating Calibration of Classifiers Markus Kängsepp, Kaspar Valk, Meelis Kull

One Transformer for All Time Series: Representing and Training with Time-Dependent Heterogeneous Tabular Data Simone Luetto, Fabrizio Garuti, Enver Sangineto, Lorenzo Forni, Rita Cucchiara

Online Dimensionality Reduction Through Stacked Generalization of Spectral Methods with Deep Networks Juan Carlos Alvarado-Pérez, Miguel Ángel García, Domenec Puig

Optimal Large-Scale Stochastic Optimization of NDCG Surrogates for Deep Learning Zi-Hao Qiu, Quanqi Hu, Yongjian Zhong, Wei-Wei Tu, Lijun Zhang, Tianbao Yang

Optimized YOLOv8 for Lightweight and High-Precision Metal Surface Defect Detection in Industrial Applications Ruiping Li, Linchang Zhao, Hao Wei, Bocheng OuYang, Mu Zhang, Bing Fang, Guoqing Hu, Jin Tan

Optimizing Sharpe Ratio: Risk-Adjusted Decision-Making in Multi-Armed Bandits Sabrina Khurshid, Mohammed Shahid Abdulla, Gourab Ghatak

Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance Marius Köppel, Alexander Segner, Martin Wagener, Lukas Pensel, Andreas Karwath, Stefan Kramer

Panda: Partially Approximate Newton Methods for Distributed Minimax Optimization with Unbalanced Dimensions Minheng Xiao, Chengchang Liu, Cheng Chen, John C. S. Lui, Sen Na

Partially Trained Graph Convolutional Networks Resist Oversmoothing Dimitrios Kelesis, Dimitris Fotakis, Georgios Paliouras

Pasco (PArallel Structured COarsening): An Overlay to Speed up Graph Clustering Algorithms Etienne Lasalle, Rémi Vaudaine, Titouan Vayer, Pierre Borgnat, Paulo Gonçalves, Rémi Gribonval, Márton Karsai

PCE-GNN: A Node Feature-Enhanced Graph Neural Network with Pre-Clustering Strategy Yongbo Li, Fangfang Xie, Xi Li, Kaiyan Chen, Jiangyi Yao, Xiongwei Li

Pdarts: Projected Differentiable Architecture Search for Seismic Inversion Lucas C. Souza, Carlos G. C. Junior, Ricardo Cerri, Edson S. Gomi, Bruno S. Carmo, Hermes Senger, Murilo Naldi

Per-Example Gradient Regularization Improves Learning Signals from Noisy Data Xuran Meng, Yuan Cao, Difan Zou

Perfect Counterfactuals in Imperfect Worlds: Modelling Noisy Implementation of Actions in Sequential Algorithmic Recourse Yueqing Xuan, Kacper Sokol, Mark Sanderson, Jeffrey Chan

PerfoRank: Cluster-Based Performance Ranking for Improved Performance Evaluation and Estimation in Professional Cycling Bram Janssens, Matthias Bogaert

Performance and Interaction Assessment of Neural Network Architectures and Bivariate Smart Predict-Then-Optimize Junhan Wen, Thomas Abeel, Mathijs de Weerdt

Physics Encoded Blocks in Residual Neural Network Architectures for Digital Twin Models Muhammad Saad Zia, Corentin Houpert, Ashiq Anjum, Lu Liu, Anthony Conway, Anasol Peña-Ríos

Physics-Informed Variational Autoencoders for Improved Robustness to Environmental Factors of Variation Romain Thoreau, Laurent Risser, Véronique Achard, Béatrice Berthelot, Xavier Briottet

Post-Selection Confidence Bounds for Prediction Performance Pascal Rink, Werner Brannath

Preference-Based Opponent Shaping in Differentiable Games Xinyu Qiao, Yudong Hu, Congying Han, Weiyan Wu, Tiande Guo

Prioritizing the Essential: A Robust Evaluation Framework for Novelty Detection Jean-Gabriel Gaudreault, Paula Branco

Probabilistic Instance Dependent Label Refinement for Noisy Label Learning Hao-Yuan He, Yu Liu, Renbiao Liu, Zheng Xie, Ming Li

Probabilistic Scoring Lists for Interpretable Machine Learning Jonas Hanselle, Stefan Heid, Johannes Fürnkranz, Eyke Hüllermeier

Progressive Semantic Learning for Unsupervised Skeleton-Based Action Recognition Hao Qin, Luyuan Chen, Ming Kong, Zhuoran Zhao, Xianzhou Zeng, Mengxu Lu, Qiang Zhu

Prompt-Based Contrastive Learning to Combat the COVID-19 Infodemic Zifan Peng, Mingchen Li, Yue Wang, Daniel Y. Mo

PT4Rec: A Universal Prompt-Tuning Framework for Graph Contrastive Learning-Based Recommendations Wei Xiao, Qifeng Zhou

Qualitative Control Learning Can Be Much Faster than Reinforcement Learning Domen Soberl, Ivan Bratko

Raising the Numbers: Multi-Generation Adversarial Attack and Frequency-Based Defense for Heightened NLP Security Salim Khemis, Yacine Amara, Mohamed Akrem Benatia, Ishak Messalti, Mohammed Elamin Ilyas Khanous, Bernard De Baets

RCC-MAS: A New Algorithm for Computing All Rough-Set-Constructs Yanir González-Díaz, Manuel S. Lazo-Cortés, José Fco. Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa

Re-Assessing Accuracy Degradation: A Framework for Understanding DNN Behavior on Similar-but-Non-Identical Test Datasets Esla Timothy Anzaku, Haohan Wang, Ajiboye Babalola, Arnout Van Messem, Wesley De Neve

ReDMan: Reliable Dexterous Manipulation with Safe Reinforcement Learning Yiran Geng, Jiaming Ji, Yuanpei Chen, Haoran Geng, Fangwei Zhong, Yaodong Yang

Region-Aware Minimal Counterfactual Rules for Model-Agnostic Explainable Classification Guido Gagliardi, Antonio Luca Alfeo, Riccardo Guidotti, Mario G. C. A. Cimino

Reinforced Logical Reasoning over KGs for Interpretable Recommendation System Shirui Wang, Bohan Xie, Ling Ding, Jianting Chen, Yang Xiang

Rényi Divergence in Hidden Markov Models Cheng-Der Fuh, Su-Chi Fuh, Yuan-Chen Liu, Chuan-Ju Wang

Resolving Power: A General Approach to Compare the Distinguishing Ability of Threshold-Free Evaluation Metrics Colin Beam

Revealing Vulnerable Regions Through Diverse Adversarial Examples Yunce Zhao, Wei Huang, Wei Liu, Xin Yao

RGCVAE: Relational Graph Conditioned Variational Autoencoder for Molecule Design Davide Rigoni, Nicolò Navarin, Alessandro Sperduti

RIADNet: Single Image Deraining Network for Raindrops and Rain Streaks Removal Changle Yu, Ping Fan, Yi Zhang, Jiyu Yang

Robust Partial-Label Learning by Leveraging Class Activation Values Tobias Fuchs, Florian Kalinke

Robust Query Performance Prediction for Dense Retrievers via Adaptive Disturbance Generation Abbas Saleminezhad, Negar Arabzadeh, Radin Hamidi Rad, Soosan Beheshti, Ebrahim Bagheri

Rope-Net: Deep Convolutional Neural Network via Robust Principal Component Analysis Baichen Liu, Zhi Han, Xi'ai Chen, Yanmei Wang, Yandong Tang

SA-CAM: Semantic-Aware Visual Explanations for Deep Convolutional Networks Anni Yu, Qing-Long Zhang, Lu Rao, Yu-Bin Yang

SA-LfV: Self-Annotated Labeling from Videos for Object Detection Arun Kumar Sivapuram, Komuravelli Prashanth, Rama Krishna Sai S. Gorthi

SafeGen: Safeguarding Privacy and Fairness Through a Genetic Method Martina Cinquini, Marta Marchiori Manerba, Federico Mazzoni, Francesca Pratesi, Riccardo Guidotti

Sandbox: Safeguarded Multi-Label Learning Through Safe Optimal Transport Lefei Zhang, Geng Yu, Jiangchao Yao, Yew-Soon Ong, Ivor W. Tsang, James T. Kwok

Scalable Individual Treatment Effect Estimator for Large Graphs Xiaofeng Lin, Han Bao, Yan Cui, Koh Takeuchi, Hisashi Kashima

Scaling the Weight Parameters in Markov Logic Networks and Relational Logistic Regression Models Felix Q. Weitkämper

Schema-Tune: Noise-Driven Bias Mitigation in Transformer-Based Language Models Omid Shokrollahi, Ruthvik Penumatcha, Faezeh Ensan, Zeinab Noorian

ScoreCL: Augmentation-Adaptive Contrastive Learning via Score-Matching Function Jin-Young Kim, Soonwoo Kwon, Hyojun Go, Yunsung Lee, Seungtaek Choi, Hyun-Gyoon Kim

Search or Split: Policy Gradient with Adaptive Policy Space Gianmarco Tedeschi, Matteo Papini, Alberto Maria Metelli, Marcello Restelli

Segmentation and Feature Extraction-Based Classification of Pavement Damages Using Hybrid Computer Vision and Machine Learning Approaches Lizette Tello-Cifuentes, Johannio Marulanda, Peter Thomson

Self Explainable Graph Convolutional Recurrent Network for Spatio-Temporal Forecasting Javier García-Sigüenza, Manuel Curado, Faraón Llorens-Largo, Jose F. Vicent

Semantic-Aware Contrastive Learning via Multi-Prompt Alignment Zhuoran Zhao, Hao Qin, Ming Kong, Luyuan Chen, Di Xie, Jiang Zhu, Qiang Zhu

Semi-Supervised Learning from Tabular Data with Autoencoders: When Does It Work? Sintija Stevanoska, Jurica Levatic, Saso Dzeroski

Shift Guided Active Learning Jianan Yang, Jimin Tan, Haobo Wang, Gang Chen, Sai Wu, Junbo Zhao

ShuttleFlow: Learning the Distribution of Subsequent Badminton Shots Using Normalizing Flows Yun-Hsuan Lien, Chia-Tung Lian, Yu-Shuen Wang

Simplification of Forest Classifiers and Regressors by Sharing Branching Conditions Naoki Marui, Atsuyoshi Nakamura, Kento Sakurada

Simultaneous Outlier Detection and Elimination in Hyperspectral Unmixing via Weighted Non-Negative Matrix Tri-Factorization Yasin Hashemi-Nazari, Farid Saberi-Movahed, Azita Tajaddini, Catarina Moreira, Xin Ning, Prayag Tiwari

Single Image Inpainting and Super-Resolution with Simultaneous Uncertainty Guarantees by Universal Reproducing Kernels Bálint Horváth, Balázs Csanád Csáji

SketchMLP: Effectively Utilize Rasterized Images and Drawing Sequences for Sketch Recognition Tengjie Li, Shikui Tu, Lei Xu

Source-Free Domain Adaptation Requires Penalized Diversity Laya Rafiee Sevyeri, Ivaxi Sheth, Farhood Farahnak, Alexandre See, Samira Ebrahimi Kahou, Thomas Fevens, Mohammad Havaei

SPA: A Poisoning Attack Framework for Graph Neural Networks Through Searching and Pairing Xiao Liu, Jun-Jie Huang, Wentao Zhao, Ziyue Wang, Zihan Chen, Yi Pan

Sparse and Smooth Additive Isotonic Model in High-Dimensional Settings Jiaqi Zhang, Yiqin Wang, Meng Wang, Beilun Wang

Spatiotemporal-View Member Preference Contrastive Representation Learning for Group Recommendation Yangtao Zhou, Qingshan Li, Hua Chu, Jianan Li, Biaobiao Wei, Shuai Zhang, Jialong Han

Splitting Stump Forests: Tree Ensemble Compression for Edge Devices (extended Version) Fouad Alkhoury, Sebastian Buschjäger, Pascal Welke

Squared Wasserstein-2 Loss Functions for Efficient Learning of Stochastic Differential Equations Mingtao Xia, Xiangting Li, Qijing Shen, Tom Chou

ST-PPO: A Spatio-Temporal Attention Enhanced Proximal Policy Optimization Algorithm for Autonomous Driving in Complex Traffic Scenarios Cheng Da, Yongsheng Qian, Junwei Zeng, Xunting Wei, Futao Zhang

State-Novelty Guided Action Persistence in Deep Reinforcement Learning Jianshu Hu, Paul Weng, Yutong Ban

Synthesizing Efficient Data with Diffusion Models for Person Re-Identification Pre-Training Ke Niu, Haiyang Yu, Xuelin Qian, Teng Fu, Bin Li, Xiangyang Xue

TCR: Topologically Consistent Reweighting for XGBoost in Regression Tasks Monty-Maximilian Zühlke, Daniel Kudenko

Temporal Distillation: Compressing a Policy in Space and Time Thomas Avé, Matthias Hutsebaut-Buysse, Kevin Mets

Temporal Ensemble of Multiple Patterns' Instances for Continuous Prediction of Events Nevo Itzhak, Szymon Jaroszewicz, Robert Moskovitch

Testing Exchangeability in the Batch Mode with E-Values and Markov Alternatives Vladimir Vovk

TFAS: Zero-Shot NAS for General Time-Series Analysis with Time-Frequency Aware Scoring Patara Trirat, Jae-Gil Lee

The Forget-Set Identification Problem Andrea D'Angelo, Francesco Gullo, Giovanni Stilo

The KANDY Benchmark: Incremental Neuro-Symbolic Learning and Reasoning with Kandinsky Patterns Luca Salvatore Lorello, Marco Lippi, Stefano Melacci

Theoretical Guarantees for Domain Adaptation with Hierarchical Optimal Transport Mourad El Hamri, Younès Bennani, Issam Falih

Tight Mixed-Integer Optimization Formulations for Prescriptive Trees Max Biggs, Georgia Perakis

Time Series Representations Classroom (TSRC): A Teacher-Student-Based Framework for Interpretability-Enhanced Unsupervised Time Series Representation Learning Wadie Skaf, Mitra Baratchi, Holger H. Hoos

Toward Finding Strong Pareto Optimal Policies in Multi-Agent Reinforcement Learning Bang Giang Le, Viet Cuong Ta

Toward Practical Human-Interpretable Explanations Alon Malach, Amiel Meiseles, Ron Bitton, Satoru Momiyama, Toshinori Araki, Jun Furukawa, Yuval Elovici, Asaf Shabtai

Towards Efficient Pareto-Optimal Utility-Fairness Between Groups in Repeated Rankings Phuong Mai Dinh, Duc-Trong Le, Tuan-Anh Hoang, Dung Duy Le

Towards Interpretable and Robust UAV-Based Foundation Model for Endangered Species Monitoring in Complex Ecosystems Jihan Zhang, Mingqiao Han, K. H. Laurie, Benyun Zhao, Lei Lei, Xi Chen, Hon Chi Judy Wan, Siu Gin Cheung, Wenxing Hong, Ben M. Chen

TransFed: Cross-Domain Feature Alignment for Semi-Supervised Federated Transfer Learning Linghui Zeng, Ruixuan Liu, Li Xiong, Joyce C. Ho

Transfer Learning with Pre-Trained Conditional Generative Models Shin'ya Yamaguchi, Sekitoshi Kanai, Atsutoshi Kumagai, Daiki Chijiwa, Hisashi Kashima

Trustworthy Representation Learning via Information Funnels and Bottlenecks João Machado de Freitas, Bernhard C. Geiger

Uncover and Unlearn Nuisances: Agnostic Fully Test-Time Adaptation Ponhvoan Srey, Yaxin Shi, Hangwei Qian, Jing Li, Ivor W. Tsang

Uncover the Balanced Geometry in Long-Tailed Contrastive Language-Image Pretraining Zhihan Zhou, Yushi Ye, Feng Hong, Peisen Zhao, Jiangchao Yao, Ya Zhang, Qi Tian, Yanfeng Wang

Uncovering Temporal Patterns in Visualizations of High-Dimensional Data Pavlin G. Policar, Blaz Zupan

Unmasking Deception: A Topic-Oriented Multimodal Approach to Uncover False Information on Social Media Riccardo Cantini, Cristian Cosentino, Irene Kilanioti, Fabrizio Marozzo, Domenico Talia

Unsupervised Variational Domain Adaptation Yundong Li, Yizheng Ge, Chen Lin, Guan Wang

User Migration in Blockchain-Based Online Social Networks Through the Lens of Temporal Node Representation Shift Manuel Dileo, Matteo Zignani

Verification on Out-of-Distribution Detectors Under Natural Perturbations Chi Zhang, Zhen Chen, Peipei Xu, Geyong Min, Wenjie Ruan

Warping Resilient Robust Anomaly Detection for Multivariate Time Series Abilasha S, Sahely Bhadra

When Interpretability Meets Noise: An LLM-Assisted Hybrid Deep Logical Rule Learning Framework Yulin Chen, Bo Yuan, Chen Chen, Zhaoqun Li, Beishui Liao