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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. 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

Sep 22

Thompson Sampling for Infinite-Horizon Discounted Decision Processes

arXiv:2405.08253v4 Announce Type: replace-cross Abstract: This paper develops a framework for learning in discounted infinite-horizon Markov decision processes (MDPs) with Borel state and action spac...

By Daniel Adelman, Cagla Keceli, Alba V. Olivares-Nadal

reinforcement-learning

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

One email a morning, machine-written

Related stories

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

Thompson Sampling for Infinite-Horizon Discounted Decision Processes

Online Learning with Gradient-Variation Interval Regret

Efficient Online Proportional Sampling with Applications to Smoothed Online Learning