arXiv Machine Learning July 7, 2026 By Hanqing Jin, Renyuan Xu, Yanzhao Yang

Adaptive Partitioning and Learning for Stochastic Control of Diffusion Processes

Read the original on arXiv Machine Learning →

arXiv:2512. 14991v2 Announce Type: replace Abstract: We study reinforcement learning for controlled diffusion processes with unbounded continuous state spaces, bounded continuous actions, and polynomially growing rewards: settings that arise naturally in finance, economics, and operations research.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 4

Conformal bandits: bringing statistical validity and reward efficiency under weak arm separability

arXiv:2512. 09850v2 Announce Type: replace Abstract: We introduce Conformal Bandits, a novel framework integrating Conformal Prediction (CP) into bandit problems, a classic paradigm for sequential decision-making under uncertainty.

By Simone Cuonzo, Nina Deliu

reinforcement-learning

arXiv Machine Learning

Aug 3

Convergence and Regret of the Policy Gradient for Multi-Armed Bandits in Diffusion Environment

arXiv:2607. 29593v1 Announce Type: new Abstract: This paper studies the policy gradient update for a multi-arm bandit problem in diffusion environment that is described by a stochastic differential equation (SDE) under the continuous-time reinforcement learning framework by Wang et al.

By Yanwei Jia, Du Ouyang

diffusion reinforcement-learning

arXiv Machine Learning

Jul 31

On-Policy and Off-Policy Learning for Large Action Spaces

arXiv:2607. 28408v1 Announce Type: new Abstract: This thesis studies policy learning in interactive systems where an agent observes a context, selects an action from a very large set, and receives partial feedback.

By Imad Aouali

agents diffusion safety

arXiv Machine Learning

Jun 3

Online Learning with Gradient-Variation Interval Regret

arXiv:2606. 03831v1 Announce Type: new Abstract: This paper investigates non-stationary online learning using the metric of interval regret, which requires an online algorithm to perform well over every time interval.

By Yan-Feng Xie, Shuche Wang, Peng Zhao, Zhi-Hua Zhou

safety

arXiv AI

Jul 14

Efficient Online Proportional Sampling with Applications to Smoothed Online Learning

arXiv:2607. 10963v1 Announce Type: cross Abstract: We study the problem of efficient online proportional sampling from a high-dimensional domain under a $\sigma$-smoothed adversary, where the sampling distribution is induced by a dynamically evolving weight function defined over a sequence of piecewise-structured partitions.

By Amirmahdi Mirfakhar, Maria-Florina Balcan, Hedyeh Beyhaghi

agents reinforcement-learning

arXiv Machine Learning

Aug 11

Regret of exploratory policy improvement and $q$-learning

arXiv:2411. 01302v2 Announce Type: replace Abstract: We study the convergence of $q$-learning and related algorithms introduced by Jia and Zhou (J.

By Wenpin Tang, Xun Yu Zhou

diffusion reinforcement-learning

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Conformal bandits: bringing statistical validity and reward efficiency under weak arm separability

Convergence and Regret of the Policy Gradient for Multi-Armed Bandits in Diffusion Environment

On-Policy and Off-Policy Learning for Large Action Spaces

Online Learning with Gradient-Variation Interval Regret

Efficient Online Proportional Sampling with Applications to Smoothed Online Learning

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