Shijie Zhong, Jiangfeng Fu: Analytical Extraction of Conditional Sobol' Indices via Basis Decomposition of Polynomial Chaos Expansions https://arxiv.org/abs/2604.19165 https://arxiv.org/pdf/2604.19165 https://arxiv.org/html/2604.19165

Huan Qing: Fast estimation of Gaussian mixture components via centering and singular value thresholding https://arxiv.org/abs/2604.19091 https://arxiv.org/pdf/2604.19091 https://arxiv.org/html/2604.19091

Yaowei Zheng, Richong Zhang, Shenxi Wu, Shirui Bian, Haosong Zhang, Li Zeng, Xingjian Ma, Yichi Zhang: Beyond Bellman: High-Order Generator Regression for Continuous-Time Policy Evaluation https://arxiv.org/abs/2604.18972 https://arxiv.org/pdf/2604.18972 https://arxiv.org/html/2604.18972

Aoran Zhang, Tianyao Wei, Maria J. Guerrero, C\'esar A. Uribe: Sparse Network Inference under Imperfect Detection and its Application to Ecological Networks https://arxiv.org/abs/2604.18820 https://arxiv.org/pdf/2604.18820 https://arxiv.org/html/2604.18820

[2026-04-22 Wed (UTC), 4 new articles found for statML Machine Learning]

Maria-Eleni Sfyraki, Jun-Kun Wang: Revisiting Active Sequential Prediction-Powered Mean Estimation https://arxiv.org/abs/2604.18569 https://arxiv.org/pdf/2604.18569 https://arxiv.org/html/2604.18569

Joonhyuk Lee, Virginia Ma, Sarah Zhao, Yash Nair, Asher Spector, Regev Cohen, Emmanuel J. Cand\`es: FUSE: Ensembling Verifiers with Zero Labeled Data https://arxiv.org/abs/2604.18547 https://arxiv.org/pdf/2604.18547 https://arxiv.org/html/2604.18547

Florentin Coeurdoux, Gr\'egoire Ferr\'e, Jean-Philippe Bouchaud: Random Matrix Theory of Early-Stopped Gradient Flow: A Transient BBP Scenario https://arxiv.org/abs/2604.18450 https://arxiv.org/pdf/2604.18450 https://arxiv.org/html/2604.18450

Michal Valko, R\'emi Munos, Branislav Kveton, Tom\'a\v{s} Koc\'ak: Spectral bandits for smooth graph functions https://arxiv.org/abs/2604.18420 https://arxiv.org/pdf/2604.18420 https://arxiv.org/html/2604.18420

Othmane Aboussaad, Adam Miraoui, Boumediene Hamzi, Houman Owhadi: Adaptive Kernel Selection for Kernelized Diffusion Maps https://arxiv.org/abs/2604.18402 https://arxiv.org/pdf/2604.18402 https://arxiv.org/html/2604.18402

Arruda, Chervet, Staudt, Wieser, Hoelscher, Sermet-Gaudelus, Binder, Opatowski, Hasenauer: Overcoming Selection Bias in Statistical Studies With Amortized Bayesian Inference https://arxiv.org/abs/2604.18319 https://arxiv.org/pdf/2604.18319 https://arxiv.org/html/2604.18319

Daniel Marks, Dario Paccagnan, Mark van der Wilk: Symmetry Guarantees Statistic Recovery in Variational Inference https://arxiv.org/abs/2604.18310 https://arxiv.org/pdf/2604.18310 https://arxiv.org/html/2604.18310

Sebastian Fischer, Lukas Burk, Carson Zhang, Bernd Bischl, Martin Binder: mlr3torch: A Deep Learning Framework in R based on mlr3 and torch https://arxiv.org/abs/2604.18152 https://arxiv.org/pdf/2604.18152 https://arxiv.org/html/2604.18152

Qi Kuang, Chao Wang, Yuling Jiao, Fan Zhou: Distributional Off-Policy Evaluation with Deep Quantile Process Regression https://arxiv.org/abs/2604.18143 https://arxiv.org/pdf/2604.18143 https://arxiv.org/html/2604.18143

Yuan-Hao Wei: StrEBM: A Structured Latent Energy-Based Model for Blind Source Separation https://arxiv.org/abs/2604.17381 https://arxiv.org/pdf/2604.17381 https://arxiv.org/html/2604.17381

Chenyang Wang, Yun Yang: PAC-Bayes Bounds for Gibbs Posteriors via Singular Learning Theory https://arxiv.org/abs/2604.17219 https://arxiv.org/pdf/2604.17219 https://arxiv.org/html/2604.17219

Kaito Goto, Naoya Takeishi, Takehisa Yairi: Forecast Sports Outcomes under Efficient Market Hypothesis: Theoretical and Experimental Analysis of Odds-Only and Generalised Linear Models https://arxiv.org/abs/2604.17194 https://arxiv.org/pdf/2604.17194 https://arxiv.org/html/2604.17194

Noga Mudrik, Adam S. Charles: Neighbor Embedding for High-Dimensional Sparse Poisson Data https://arxiv.org/abs/2604.16932 https://arxiv.org/pdf/2604.16932 https://arxiv.org/html/2604.16932

Korolev, Ivanov, Kukanova, Rukavitsa, Vakshin, Solomonov, Zeifman: Extraction of informative statistical features in the problem of forecasting time series generated by It{\^{o}}-type processes https://arxiv.org/abs/2604.16865 https://arxiv.org/pdf/2604.16865 https://arxiv.org/html/2604.16865

Peifeng Gao, Wenyi Fang, Yang Zheng, Difan Zou: A Mechanism Study of Delayed Loss Spikes in Batch-Normalized Linear Models https://arxiv.org/abs/2604.16809 https://arxiv.org/pdf/2604.16809 https://arxiv.org/html/2604.16809

Yixiao Lin, James Booth: Fairness Constraints in High-Dimensional Generalized Linear Models https://arxiv.org/abs/2604.16610 https://arxiv.org/pdf/2604.16610 https://arxiv.org/html/2604.16610

[2026-04-21 Tue (UTC), 16 new articles found for statML Machine Learning]

Come Fiegel, Victor Gabillon, Michal Valko: Adaptive multi-fidelity optimization with fast learning rates https://arxiv.org/abs/2604.16239 https://arxiv.org/pdf/2604.16239 https://arxiv.org/html/2604.16239

Tianhao Liu, Daniel Andr\'es D\'iaz-Pach\'on, J. Sunil Rao: PRIM-cipal components analysis https://arxiv.org/abs/2604.15538 https://arxiv.org/pdf/2604.15538 https://arxiv.org/html/2604.15538

Panos Tsimpos, Daniel Sharp, Youssef Marzouk: One-Shot Generative Flows: Existence and Obstructions https://arxiv.org/abs/2604.15439 https://arxiv.org/pdf/2604.15439 https://arxiv.org/html/2604.15439

[2026-04-20 Mon (UTC), 3 new articles found for statML Machine Learning]

V\'ictor Soto-Larrosa, Nuria Torrado, Edmundo J. Huertas: Structural interpretability in SVMs with truncated orthogonal polynomial kernels https://arxiv.org/abs/2604.15285 https://arxiv.org/pdf/2604.15285 https://arxiv.org/html/2604.15285

Minh-Phuc Truong, Khai Nguyen: Amortized Optimal Transport from Sliced Potentials https://arxiv.org/abs/2604.15114 https://arxiv.org/pdf/2604.15114 https://arxiv.org/html/2604.15114

Chenghui Zheng, Garvesh Raskutti: MinShap: A Modified Shapley Value Approach for Feature Selection https://arxiv.org/abs/2604.15107 https://arxiv.org/pdf/2604.15107 https://arxiv.org/html/2604.15107

Y-h. Taguchi, Yoh-ichi Mototake: Unsupervised feature selection using Bayesian Tucker decomposition https://arxiv.org/abs/2604.14949 https://arxiv.org/pdf/2604.14949 https://arxiv.org/html/2604.14949