William Gibson, Christoph Reisinger Uniform Scaling Limits in AdamW-Trained Transformers https://arxiv.org/abs/2605.11059

Kane Warrior, Dalia Chakrabarty Interpretable Machine Learning for Spatial Science: A Lie-Algebraic Kernel for Rotationally Anisotropic Gaussian Processes https://arxiv.org/abs/2605.11179

Aidan Gleich, Eric Laber, Alexander Volfovsky Adaptive Policy Learning Under Unknown Network Interference https://arxiv.org/abs/2605.11191

Wen-Ting Wang, Wei-Ying Wu, Hao-Yun Huang, Xuan-Chun Wang Spatial Adapter: Structured Spatial Decomposition and Closed-Form Covariance for Frozen Predictors https://arxiv.org/abs/2605.11394

Shuichi Nishino, Tomohiro Shiraishi, Teruyuki Katsuoka, Ichiro Takeuchi Post-ADC Inference: Valid Inference After Active Data Collection https://arxiv.org/abs/2605.11511

Haoran Hu, Xingce Wang Exact Stiefel Optimization for Probabilistic PLS: Closed-Form Updates, Error Bounds, and Calibrated Uncertainty https://arxiv.org/abs/2605.11607

Xiaoning Wang, Yuyang Huo, Liuhua Peng, Changliang Zou Learning U-Statistics with Active Inference https://arxiv.org/abs/2605.11638

Jeunghun Oh, Kyeongwon Lee, Jaeyong Lee, Lizhen Lin Posterior Contraction Rates for Sparse Kolmogorov-Arnold Networks in Anisotropic Besov Spaces https://arxiv.org/abs/2605.11652

Binh Duc Vu, David S. Watson Minimax Rates and Spectral Distillation for Tree Ensembles https://arxiv.org/abs/2605.11841

Shuxing Fang, Ruijian Han, Liangyu Zhang, Fan Zhou Variance-aware Reward Modeling with Anchor Guidance https://arxiv.org/abs/2605.11865

Alexander Shen, Mikael Kuusela Keeping Score: Efficiency Improvements in Neural Likelihood Surrogate Training via Score-Augmented Loss Functions https://arxiv.org/abs/2605.12118

Futoshi Futami, Masahiro Fujisawa Information-Theoretic Generalization Bounds for Sequential Decision Making https://arxiv.org/abs/2605.12190

Julian Rodemann, Alexander Marquard, Thomas Augustin, Michele Caprio Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification https://arxiv.org/abs/2605.12208

Giovanni Cerulli Optimal Policy Learning under Budget and Coverage Constraints https://arxiv.org/abs/2605.12235

Dang Hoang Duy, Yannis Montreuil, Maxime Meyer, Axel Carlier, Lai Xing Ng, Wei Tsang Ooi Online Learning-to-Defer with Varying Experts https://arxiv.org/abs/2605.12340

Laura L\"utzow, Simone Garatti, Marco C. Campi, Lars Lindemann, Matthias Althoff Multi-Variable Conformal Prediction: Optimizing Prediction Sets without Data Splitting https://arxiv.org/abs/2605.12341

Ziwei Su, Imon Banerjee, Diego Klabjan Model-based Bootstrap of Controlled Markov Chains https://arxiv.org/abs/2605.12410

Houman Safaai, Alessandro Marin Vargas Dynamic Vine Copulas: Detecting and Quantifying Time-Varying Higher-Order Interactions https://arxiv.org/abs/2605.03061

Ran Zou, Wanrong Zhu, Bin Nan Conformalized Percentile Interval: Finite Sample Validity and Improved Conditional Performance https://arxiv.org/abs/2605.03233

Kisung You Intrinsic effective sample size for manifold-valued Markov chain Monte Carlo via kernel discrepancy https://arxiv.org/abs/2605.03266

Mingzhe Yang, Shuo Wang, Jiang Zhang Partial Effective Information Decomposition for Synergistic Causality https://arxiv.org/abs/2605.03267

Brian Keith-Norambuena, Juan Bekios-Calfa On the Spectral Structure and Objective Equivalence of Orthogonal Multilabel Fisher Discriminants https://arxiv.org/abs/2605.03283

Daniel Fraiman, Ricardo Fraiman Imbalanced Classification under Capacity Constraints https://arxiv.org/abs/2605.03289

Riddhiman Bhattacharyya, Sayak Chakrabarty, Imon Banerjee Adaptive Estimation and Optimal Control in Offline Contextual MDPs without Stationarity https://arxiv.org/abs/2605.03393

Jian Xu, Wei Chen. Chao Li, Jingyuan Zheng, Delu Zeng, John Paisley, Qibin Zhao Stochastic Schr\"odinger Diffusion Models for Pure-State Ensemble Generation https://arxiv.org/abs/2605.03573

Siavash Ameli, Chris van der Heide, Liam Hodgkinson, Michael W. Mahoney Free Decompression with Algebraic Spectral Curves https://arxiv.org/abs/2605.03634

Nan Feng, Xun Huan Amortized Variational Inference for Joint Posterior and Predictive Distributions in Bayesian Uncertainty Quantification https://arxiv.org/abs/2605.03710

Andreas Makris, Paul Fearnhead, Chris Nemeth Tempered Guided Diffusion https://arxiv.org/abs/2605.03712

Stef van Buuren Predicting missing values: A good idea? https://arxiv.org/abs/2605.03733

Niclas F\"uhrling, Getuar Rexhepi, Giuseppe Thadeu Freitas de Abreu Low Rank Tensor Completion via Adaptive ADMM https://arxiv.org/abs/2605.03736

Valery Manokhin Training-Free Probabilistic Time-Series Forecasting with Conformal Seasonal Pools https://arxiv.org/abs/2605.03789

Valery Manokhin The Manokhin Probability Matrix: A Diagnostic Framework for Classifier Probability Quality https://arxiv.org/abs/2605.03816

Francisco M. Castro-Mac\'ias, Pablo Morales-\'Alvarez, Saifuddin Syed, Daniel Hern\'andez-Lobato, Rafael Molina, Jos\'e Miguel Hern\'andez-Lobato Conditional Diffusion Sampling https://arxiv.org/abs/2605.04013

Minhee Park, Seongyeon Son, Yonghyun Lee, Eunchan Kim SCOPE-FE: Structured Control of Operator and Pairwise Exploration for Feature Engineering https://arxiv.org/abs/2604.27025

Tianyu Yang, Md. Noor-E-Alam A Novel Computational Framework for Causal Inference: Tree-Based Discretization with ILP-Based Matching https://arxiv.org/abs/2604.27307

Eichi Uehara Bayesian X-Learner: Calibrated Posterior Inference for Heterogeneous Treatment Effects under Heavy-Tailed Outcomes https://arxiv.org/abs/2604.27394

Yanwu Gu, Linglong Kong, Dong Xia Prediction-powered Inference by Mixture of Experts https://arxiv.org/abs/2604.27892

Joseph Lazzaro, Davide Buffelli, Da-shan Shiu, Sattar Vakili A Finite Time Analysis of Thompson Sampling for Bayesian Optimization with Preferential Feedback https://arxiv.org/abs/2604.25025

Mohammad Jafari Jozani, Bahram Moeinianfar Elite-Driven Support Vector Machines for Classification https://arxiv.org/abs/2604.25158

Tom\'a\v{s} Koc\'ak, Gergely Neu, Michal Valko Online learning with Erd\H{o}s-R\'enyi side-observation graphs https://arxiv.org/abs/2604.25271

Tom\'a\v{s} Koc\'ak, R\'emi Munos, Branislav Kveton, Shipra Agrawal, Michal Valko Spectral bandits https://arxiv.org/abs/2604.25272

Yuhe Bai, Chengli Tan, Jiaqi Li, Xiangjun Wang, Zhikun Zhang Residual-loss Anomaly Analysis of Physics-Informed Neural Networks: An Inverse Method for Change-point Detection in Nonlinear Dynamical Systems with Regime Switching https://arxiv.org/abs/2604.25655

Sharmin Afroz, Brendan Ames Deflation-Free Optimal Scoring https://arxiv.org/abs/2604.25664

Wei-Ting Tang, Joel A. Paulson Rethinking Trust Region Bayesian Optimization in High Dimensions https://arxiv.org/abs/2604.22967

Mackenzie R. Neal, Paul D. McNicholas, Arthur White Turtle shell clustering: A mixture approach to discriminative clustering with applications to flow cytometry and other data https://arxiv.org/abs/2604.23083

Lei Wang, Debashis Ghosh MOCA: A Transformer-based Modular Causal Inference Framework with One-way Cross-attention and Cutting Feedback https://arxiv.org/abs/2604.23107

Weihao Lu, Qian Lin, Yingcun Xia, Dongming Huang Learning Curves and Benign Overfitting of Spectral Algorithms in Large Dimensions https://arxiv.org/abs/2604.23212

Anthony Lee Explicit integral representations and quantitative bounds for two-layer ReLU networks https://arxiv.org/abs/2604.23260

Haoxuan Wang, Xinchen Du, Sen Na Inference of Online Newton Methods with Nesterov's Accelerated Sketching https://arxiv.org/abs/2604.23436

Teng Li, Stephen Wu, Yong Huang, James L. Beck, Hui Li Probabilistic Graphical Model using Graph Neural Networks for Bayesian Inversion of Discrete Structural Component States https://arxiv.org/abs/2604.23514