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Three arXiv papers advance bandit algorithms for machine learning

By PulseAugur Editorial · [3 sources] ·

Three new research papers published on arXiv explore advancements in bandit algorithms for machine learning. The first paper, "Gaussian-Width Bounds for Offline-to-Online Linear Bandits," introduces a new algorithm with improved regret bounds for scenarios where offline data is partially covered. The second, "Accelerating Linear Bandits with Persistent Candidate Sets," proposes PC-LinUCB to reduce computational costs in linear bandits with large action sets. The third paper, "Bandits with Anytime Knapsacks," presents a novel algorithm called SUAK that addresses anytime cost constraints in bandit problems, achieving competitive rewards compared to existing methods. AI

IMPACT These papers introduce theoretical and algorithmic improvements for bandit problems, potentially enhancing decision-making in areas like recommendation systems and online learning.

RANK_REASON Cluster consists of three academic papers published on arXiv detailing new algorithms and theoretical bounds for bandit problems in machine learning.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

Three arXiv papers advance bandit algorithms for machine learning

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COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Kushagra Chandak, Volodymyr Tkachuk, Xiaoqi Tan, Csaba Szepesv\'ari ·

    Gaussian-Width Bounds for Offline-to-Online Linear Bandits

    arXiv:2610.12990v1 Announce Type: new Abstract: We study how offline data can help online learning in stochastic linear bandits. We focus on partially covered offline data, where only $k$ of the $d$ feature directions are poorly covered. Existing analyses of algorithms based on p…

  2. arXiv cs.LG TIER_1 English(EN) · Bingkui Tong, Daria Korovaitceva, Marina Sheshukova, Sergey Samsonov, Junpei Komiyama ·

    Accelerating Linear Bandits with Persistent Candidate Sets

    arXiv:2610.13407v1 Announce Type: new Abstract: Exhaustive optimistic scoring can make action selection costly in linear bandits with a large fixed set of $K$ actions, requiring $\mathcal{O}(K)$ score evaluations per round. We introduce Persistent Candidate LinUCB (\textsc{PC-Lin…

  3. arXiv cs.LG TIER_1 English(EN) · Eray Can Elumar, Cem Tekin, Osman Yagan ·

    Bandits with Anytime Knapsacks

    arXiv:2501.18560v2 Announce Type: replace Abstract: We consider bandits with anytime knapsacks (BwAK), a novel version of the BwK problem where there is an \textit{anytime} cost constraint instead of a total cost budget. This problem setting introduces additional complexities as …

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