arXiv stat.ML Machine Learning

Zewen Yang, Dongfa Zhang, Xiaobing Dai, Fengyi Yu, Chi Zhang, Bingkun Huang, Hamid Sadeghian, Sami Haddadin: Streaming Generated Gaussian Process Experts for Online Learning and Control https://arxiv.org/abs/2508.03679 https://arxiv.org/pdf/2508.03679 https://arxiv.org/html/2508.03679

Arturo P\'erez-Peralta, Sandra Ben\'itez-Pe\~na, Rosa E. Lillo: FairLangProc: A Python package for fairness in NLP https://arxiv.org/abs/2508.03677 https://arxiv.org/pdf/2508.03677 https://arxiv.org/html/2508.03677

Farzad Aryan: Pair Correlation Factor and the Sample Complexity of Gaussian Mixtures https://arxiv.org/abs/2508.03633 https://arxiv.org/pdf/2508.03633 https://arxiv.org/html/2508.03633

Bhavesh Neekhra, Debayan Gupta, Partha Pratim Chakravarti: On the (In)Significance of Feature Selection in High-Dimensional Datasets https://arxiv.org/abs/2508.03593 https://arxiv.org/pdf/2508.03593 https://arxiv.org/html/2508.03593

Sergio Cruces: The alpha-beta divergence for real and complex data https://arxiv.org/abs/2508.03272 https://arxiv.org/pdf/2508.03272 https://arxiv.org/html/2508.03272

Yahya Alkhatib, Wee Peng Tay: On Conformal Machine Unlearning https://arxiv.org/abs/2508.03245 https://arxiv.org/pdf/2508.03245 https://arxiv.org/html/2508.03245

Eliot Beyler (SIERRA), Francis Bach (SIERRA): Convergence of Deterministic and Stochastic Diffusion-Model Samplers: A Simple Analysis in Wasserstein Distance https://arxiv.org/abs/2508.03210 https://arxiv.org/pdf/2508.03210 https://arxiv.org/html/2508.03210

Sukruta Prakash Midigeshi, Tanmay Goyal, Gaurav Sinha: Achieving Limited Adaptivity for Multinomial Logistic Bandits https://arxiv.org/abs/2508.03072 https://arxiv.org/pdf/2508.03072 https://arxiv.org/html/2508.03072

Biyi Fang, Jean Utke, Truong Vo, Diego Klabjan: BoostTransformer: Enhancing Transformer Models with Subgrid Selection and Importance Sampling https://arxiv.org/abs/2508.02924 https://arxiv.org/pdf/2508.02924 https://arxiv.org/html/2508.02924

Roman Gutierrez, Tony Kai Tang, Isabel Gutierrez: Beyond Least Squares: Robust Regression Transformer (R2T) https://arxiv.org/abs/2508.02874 https://arxiv.org/pdf/2508.02874 https://arxiv.org/html/2508.02874

Wenbo Cao, Weiwei Zhang: Overcoming the Loss Conditioning Bottleneck in Optimization-Based PDE Solvers: A Novel Well-Conditioned Loss Function https://arxiv.org/abs/2508.02692 https://arxiv.org/pdf/2508.02692 https://arxiv.org/html/2508.02692

G\'erard Ben Arous, Murat A. Erdogdu, N. Mert Vural, Denny Wu: Learning quadratic neural networks in high dimensions: SGD dynamics and scaling laws https://arxiv.org/abs/2508.03688 https://arxiv.org/pdf/2508.03688 https://arxiv.org/html/2508.03688

Lei Qian, Wu Su, Yanqi Huang, Song Xi Chen: Likelihood Matching for Diffusion Models https://arxiv.org/abs/2508.03636 https://arxiv.org/pdf/2508.03636 https://arxiv.org/html/2508.03636

Zhanye Luo, Yuefeng Han, Xiufan Yu: Supervised Dynamic Dimension Reduction with Deep Neural Network https://arxiv.org/abs/2508.03546 https://arxiv.org/pdf/2508.03546 https://arxiv.org/html/2508.03546

Francisco Daunas, I\~naki Esnaola, Samir M. Perlaza: A Dual Optimization View to Empirical Risk Minimization with f-Divergence Regularization https://arxiv.org/abs/2508.03314 https://arxiv.org/pdf/2508.03314 https://arxiv.org/html/2508.03314

Eduardo Abi Jaber, Louis-Amand G\'erard: Hedging with memory: shallow and deep learning with signatures https://arxiv.org/abs/2508.02759 https://arxiv.org/pdf/2508.02759 https://arxiv.org/html/2508.02759

[2025-08-06 Wed (UTC), 5 new articles found for statML Machine Learning]

Joon-Hyun Park, Mujin Cheon, Dong-Yeun Koh: BOOST: Bayesian Optimization with Optimal Kernel and Acquisition Function Selection Technique https://arxiv.org/abs/2508.02332 https://arxiv.org/pdf/2508.02332 https://arxiv.org/html/2508.02332

Saleh Nikooroo, Thomas Engel: Understanding Learning Dynamics Through Structured Representations https://arxiv.org/abs/2508.02126 https://arxiv.org/pdf/2508.02126 https://arxiv.org/html/2508.02126

Ju Chen, Jun Feng, Shenyu Zhang: Understanding the Essence: Delving into Annotator Prototype Learning for Multi-Class Annotation Aggregation https://arxiv.org/abs/2508.02123 https://arxiv.org/pdf/2508.02123 https://arxiv.org/html/2508.02123

Runze Zhao, Yue Yu, Ruhan Wang, Chunfeng Huang, Dongruo Zhou: Instance-Dependent Continuous-Time Reinforcement Learning via Maximum Likelihood Estimation https://arxiv.org/abs/2508.02103 https://arxiv.org/pdf/2508.02103 https://arxiv.org/html/2508.02103

Sijia Wang, Ricardo Henao: Model Recycling Framework for Multi-Source Data-Free Supervised Transfer Learning https://arxiv.org/abs/2508.02039 https://arxiv.org/pdf/2508.02039 https://arxiv.org/html/2508.02039

Hyungjin Chung, Jeongsol Kim, Jong Chul Ye: Diffusion models for inverse problems https://arxiv.org/abs/2508.01975 https://arxiv.org/pdf/2508.01975 https://arxiv.org/html/2508.01975

Alexander Norcliffe, Changhee Lee, Fergus Imrie, Mihaela van der Schaar, Pietro Lio: Stochastic Encodings for Active Feature Acquisition https://arxiv.org/abs/2508.01957 https://arxiv.org/pdf/2508.01957 https://arxiv.org/html/2508.01957

Gian Marco Paldino, Gianluca Bontempi: Causal Discovery in Multivariate Time Series through Mutual Information Featurization https://arxiv.org/abs/2508.01848 https://arxiv.org/pdf/2508.01848 https://arxiv.org/html/2508.01848

Omid Arhami, Pejman Rohani: Topolow: Force-Directed Euclidean Embedding of Dissimilarity Data with Robustness Against Non-Metricity and Sparsity https://arxiv.org/abs/2508.01733 https://arxiv.org/pdf/2508.01733 https://arxiv.org/html/2508.01733

Aytijhya Saha, Aaditya Ramdas: Density estimation with atoms, and functional estimation for mixed discrete-continuous data https://arxiv.org/abs/2508.01706 https://arxiv.org/pdf/2508.01706 https://arxiv.org/html/2508.01706

Alberto Maria Metelli, Simone Drago, Marco Mussi: Generalized Kernelized Bandits: Self-Normalized Bernstein-Like Dimension-Free Inequality and Regret Bounds https://arxiv.org/abs/2508.01681 https://arxiv.org/pdf/2508.01681 https://arxiv.org/html/2508.01681

Juyan Zhang, Rhys Newbury, Xinyang Zhang, Tin Tran, Dana Kulic, Michael Burke: Why Heuristic Weighting Works: A Theoretical Analysis of Denoising Score Matching https://arxiv.org/abs/2508.01597 https://arxiv.org/pdf/2508.01597 https://arxiv.org/html/2508.01597

Ziwei Su, Imon Banerjee, Diego Klabjan: Central Limit Theorems for Transition Probabilities of Controlled Markov Chains https://arxiv.org/abs/2508.01517 https://arxiv.org/pdf/2508.01517 https://arxiv.org/html/2508.01517