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New research explores bandit algorithms for optimal decision-making with delays and bounded noise · 5 sources…

By PulseAugur Editorial · [5 sources] ·

Researchers have published new papers on bandit algorithms, exploring different approaches to optimize decision-making under uncertainty. One paper investigates stochastic linear bandits with delayed feedback, analyzing how various delay models impact regret guarantees and comparing them to multi-armed bandits. Another study focuses on stochastic linear contextual bandits with bounded noise, proposing a novel algorithm that leverages set-membership estimation to achieve improved regret bounds. A third paper examines stabilizing bandits using regularization, deriving precise regret bounds and a quantitative central limit theorem, highlighting the trade-off between inference validity and optimal regret rates. AI

IMPACT These papers advance theoretical understanding of bandit algorithms, potentially leading to more efficient decision-making in AI systems facing uncertainty and delayed information.

RANK_REASON Multiple arXiv papers published on related machine learning research topics.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 5 sources. How we write summaries →

New research explores bandit algorithms for optimal decision-making with delays and bounded noise · 5 sources…

COVERAGE [5]

  1. arXiv cs.LG TIER_1 English(EN) · Ofir Schlisselberg, Mengxiao Zhang, Yishay Mansour ·

    Near-Optimal Stochastic Linear Bandits with Delay

    arXiv:2606.16656v1 Announce Type: new Abstract: We study stochastic linear bandits with delayed feedback under several delay models and establish near-optimal regret guarantees. Our results identify when delayed linear bandits exhibit the same qualitative behavior as multi-armed …

  2. arXiv cs.LG TIER_1 English(EN) · Yishay Mansour ·

    Near-Optimal Stochastic Linear Bandits with Delay

    We study stochastic linear bandits with delayed feedback under several delay models and establish near-optimal regret guarantees. Our results identify when delayed linear bandits exhibit the same qualitative behavior as multi-armed bandits (MAB), and when the linear structure cre…

  3. arXiv stat.ML TIER_1 English(EN) · Haonan Xu, Yingying Li ·

    Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach

    arXiv:2606.20022v1 Announce Type: new Abstract: This paper considers stochastic linear contextual bandits (SLCB) with bounded reward noise. Existing works typically assume sub-Gaussian reward noise and bounded expected rewards, under which the optimal regret bound scales as $\til…

  4. arXiv stat.ML TIER_1 English(EN) · Budhaditya Halder, Ishan Sengupta, Koustav Chowdhury, Samya Praharaj, Koulik Khamaru ·

    Stabilizing Bandits using Regularization: Precise Regret and A Quantitative Central Limit Theorem

    arXiv:2603.10184v2 Announce Type: replace Abstract: Statistical inference with bandit data presents fundamental challenges owing to adaptive sampling, which violates the independence assumptions underlying classical asymptotic theory. Recent work has identified stability~\citep{l…

  5. arXiv stat.ML TIER_1 English(EN) · Yingying Li ·

    Stochastic Linear Contextual Bandits with Bounded Noise: A Set-Membership Approach

    This paper considers stochastic linear contextual bandits (SLCB) with bounded reward noise. Existing works typically assume sub-Gaussian reward noise and bounded expected rewards, under which the optimal regret bound scales as $\tilde{O}(\sqrt{T})$ in terms of horizon $T$. Howeve…

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