arXiv Machine Learning July 17, 2026 By Aniruddhan Ganesaraman, VIdyadhar Kulkarni

Data Driven Block Replacement Scheduling

Read the original on arXiv Machine Learning →

arXiv:2607. 15229v1 Announce Type: new Abstract: We develop data-driven algorithms for maintaining $N$ independent identical machines under a \textit{block replacement policy}, in which each machine is replaced upon failure and all machines are jointly replaced at regular intervals of length $k$.

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arXiv Machine Learning

4d ago

Learning When to Update: A Near-Optimal Timing Bandit Approach

arXiv:2609.37932v1 Announce Type: new Abstract: Systems operating in dynamic environments require timely updates to sustain performance. For resource-intensive systems such as machine learning models...

By Qiulin Lin, Junyan Su, Liyuan Wang, Minghua Chen

reinforcement-learning

arXiv Machine Learning

Jun 9

Algorithm for Contextual Queueing Bandits with Rate-Optimal Queue Length Regret

arXiv:2606. 09668v1 Announce Type: new Abstract: Contextual queueing bandits provide a framework for learning to schedule heterogeneous jobs under unknown context-dependent service rates.

By Seoungbin Bae, Dabeen Lee

arXiv Machine Learning

Jun 29

Learning in Markovian bandits with non-observable states and constrained decision epochs

arXiv:2606. 27448v1 Announce Type: new Abstract: This paper studies the problem of regret minimization in Markovian bandits with \emph{non-observable states} and possibly \emph{constrained} decision epochs.

By Thomas Hira, Victor Boone, Urtzi Ayesta, Ina Maria Verloop

reinforcement-learning benchmarks safety

arXiv Machine Learning

Jul 16

On the Sublinear Regret of Continuous K-Max Bandits

arXiv:2502. 13467v2 Announce Type: replace Abstract: The $K$-Max combinatorial multi-armed bandit problem arises in applications such as recommendation and distributed decision making, where the reward is determined by the maximum outcome among $K$ selected arms.

By Yu Chen, Siwei Wang, Longbo Huang, Wei Chen

reinforcement-learning safety

arXiv Machine Learning

Aug 27

Beyond Optimal Rates in Stochastic Optimization: Trajectory-Adaptive Stopping Rules

arXiv:2608. 25551v1 Announce Type: new Abstract: Stochastic gradient descent (SGD) is typically analyzed at a deterministic horizon chosen before the algorithm is run, even though practical stopping decisions are made adaptively by inspecting the evolving trajectory.

By Liviu Aolaritei, Lucas L\'evy, Francis Bach, Michael I. Jordan

arXiv Machine Learning

Sep 15

Toward Optimal Switching Regret for Multi-Armed Bandits with Oblivious Adversary

arXiv:2609.13547v1 Announce Type: new Abstract: We study switching regret in adversarial multi-armed bandits, where the learner competes with an arm sequence that changes at most $S$ times. When $S$...

By Mengxiao Zhang

safety

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Learning When to Update: A Near-Optimal Timing Bandit Approach

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Learning in Markovian bandits with non-observable states and constrained decision epochs

On the Sublinear Regret of Continuous K-Max Bandits

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Toward Optimal Switching Regret for Multi-Armed Bandits with Oblivious Adversary