Yordan Raykov, Rodrigo Veiga: Information-Geometric Forward Policy Training in GFlowNets https://arxiv.org/abs/2608.03967 https://arxiv.org/pdf/2608.03967 https://arxiv.org/html/2608.03967

Hanqin Cai, Longxiu Huang, Jing Qin, Chengyue Wu: Robust Low-Tubal-Rank Tensor Completion under Cross-Concentrated Sampling https://arxiv.org/abs/2608.03928 https://arxiv.org/pdf/2608.03928 https://arxiv.org/html/2608.03928

Yufei Wu, Shanqing Gao, Andreas Voss, Francis Tuerlinckx: Divide-and-Conquer: Towards Generalizable Amortized Bayesian Inference for the Drift Diffusion Model https://arxiv.org/abs/2608.03566 https://arxiv.org/pdf/2608.03566 https://arxiv.org/html/2608.03566

Ziyue Wang, Takafumi Kanamori: Should the Boundary Term Be Learned in Reflected Diffusion? Conormal Trace and Reflection Masking https://arxiv.org/abs/2608.03469 https://arxiv.org/pdf/2608.03469 https://arxiv.org/html/2608.03469

Edgar Jaber, R\'emy Vallot, Thibault Dairay, Mathilde Mougeot: Conformal risk control for model-form uncertainty in parametric non-intrusive reduced-order models https://arxiv.org/abs/2608.03360 https://arxiv.org/pdf/2608.03360 https://arxiv.org/html/2608.03360

Xueping Gong, Zhuoluo Zhang, Zhaowei Miao, Jiheng Zhang: Minimax-Optimal Semiparametric Contextual Dynamic Pricing with Multimodal Revenue https://arxiv.org/abs/2608.03142 https://arxiv.org/pdf/2608.03142 https://arxiv.org/html/2608.03142

Kevin Christian Wibisono, Yixin Wang: Causal Inference with Unstructured Outcomes https://arxiv.org/abs/2608.03085 https://arxiv.org/pdf/2608.03085 https://arxiv.org/html/2608.03085

Jiechen Jackie Zhang, O. Deniz Akyildiz: Particle-based Generalised Stochastic Optimisation https://arxiv.org/abs/2608.02844 https://arxiv.org/pdf/2608.02844 https://arxiv.org/html/2608.02844

Sunder Ram Krishnan: A Hyperfinite Framework for Score-Based Generative Modeling https://arxiv.org/abs/2608.02799 https://arxiv.org/pdf/2608.02799 https://arxiv.org/html/2608.02799

[2026-08-05 Wed (UTC), 9 new articles found for statML Machine Learning]

Jiachen Hu, Han Zhong: Interaction Is Not Necessary for Order-Optimal 1-Bit Mean Estimation https://arxiv.org/abs/2608.02538 https://arxiv.org/pdf/2608.02538 https://arxiv.org/html/2608.02538

Leda Wang, Zhehao Xu, Qiang Liu, Harrison H. Zhou: Computational and Statistical Guarantees of the \textit{c}-Rectified flow https://arxiv.org/abs/2608.02487 https://arxiv.org/pdf/2608.02487 https://arxiv.org/html/2608.02487

Jinwon Sohn, Veronika Ro\v{c}kov\'a: Private Generative Bootstrap via Blocking https://arxiv.org/abs/2608.02480 https://arxiv.org/pdf/2608.02480 https://arxiv.org/html/2608.02480

Xiaoxian Tang, Bican Xia, Tianqi Zhao: Detecting Nonproperness of Likelihood Equations https://arxiv.org/abs/2608.01976 https://arxiv.org/pdf/2608.01976 https://arxiv.org/html/2608.01976

Xizhe Zhang: The Label Defines the Timescale: Trait-State Limits of Temporal-Aggregate Learning https://arxiv.org/abs/2608.01587 https://arxiv.org/pdf/2608.01587 https://arxiv.org/html/2608.01587

Sam Andersson, Ricky Mol\'en: Finite-Probe Total-Variation Certificates for Finite-Basis Drifting Models https://arxiv.org/abs/2608.01547 https://arxiv.org/pdf/2608.01547 https://arxiv.org/html/2608.01547

Jonghyun Sim, Wonyoung Kim: Dominant Arm Identification with Mixing and Recycling Observed Samples https://arxiv.org/abs/2608.01545 https://arxiv.org/pdf/2608.01545 https://arxiv.org/html/2608.01545

Adel Kaleche: How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule https://arxiv.org/abs/2608.01268 https://arxiv.org/pdf/2608.01268 https://arxiv.org/html/2608.01268

Jiaan Han, Junxiao Chen, Yanzhe Fu: Model-Agnostic FDR Control via Group Gaussian Mirror and Permutation SHAP https://arxiv.org/abs/2608.00989 https://arxiv.org/pdf/2608.00989 https://arxiv.org/html/2608.00989

Alexander Scheinker: Round-Trip Consistency: Bidirectional Diffusion Models Can Predict Their Own Rollout Errors https://arxiv.org/abs/2608.00675 https://arxiv.org/pdf/2608.00675 https://arxiv.org/html/2608.00675

Kevin Christian Wibisono, Yixin Wang: Causal Inference with Unstructured Treatments https://arxiv.org/abs/2608.00657 https://arxiv.org/pdf/2608.00657 https://arxiv.org/html/2608.00657

Bego\~na B. Sierra, Colin McLean, Peter S. Hall, Sarah Friedrich-Welz, Catalina A. Vallejos: A reproducible and extensible framework for benchmarking competing risks survival models https://arxiv.org/abs/2608.00271 https://arxiv.org/pdf/2608.00271 https://arxiv.org/html/2608.00271

[2026-08-04 Tue (UTC), 12 new articles found for statML Machine Learning]

Haozheng Xu, Siyuan Ma, Qingyan Xiang: Analytical and Bootstrap Confidence Intervals of Double Machine Learning: Simulation studies and an application to rural-urban difference in obesity pre... https://arxiv.org/abs/2607.29456 https://arxiv.org/pdf/2607.29456 https://arxiv.org/html/2607.29456

Kai Zhou, Michael Lingzhi Li, Kai Wang: The Greedy Advantage in Finite-Horizon Bandits https://arxiv.org/abs/2607.29375 https://arxiv.org/pdf/2607.29375 https://arxiv.org/html/2607.29375

Emmanuel Vazquez, S\'ebastien Petit: Simple-regret rates and minimax optimality of fixed-prior expected improvement in Mat\'ern and squared-exponential RKHSs https://arxiv.org/abs/2607.29245 https://arxiv.org/pdf/2607.29245 https://arxiv.org/html/2607.29245

Tyler Ashoff, Jordan Rodu: Persistent Convolution: A Topological Framework for AI Alignment Testing and Semantic Space Characterization https://arxiv.org/abs/2607.29008 https://arxiv.org/pdf/2607.29008 https://arxiv.org/html/2607.29008

Isabel Corona Guevara, Yeping Hu: Structured Neural Chaos: An Adaptive Surrogate Modeling Framework for Functional Uncertainty Quantification and Global Sensitivity Analysis https://arxiv.org/abs/2607.28903 https://arxiv.org/pdf/2607.28903 https://arxiv.org/html/2607.28903

Silas Koemen: Conditioning Tree-Based Diffusions and Flows for Probabilistic Tabular Regression https://arxiv.org/abs/2607.28864 https://arxiv.org/pdf/2607.28864 https://arxiv.org/html/2607.28864

Borna Khodabandeh, Mehdi Molkaraie: Accelerated Random-Sweep Gibbs Sampling for Gaussian Graphical Models via Dual Normal Factor Graphs https://arxiv.org/abs/2607.28706 https://arxiv.org/pdf/2607.28706 https://arxiv.org/html/2607.28706