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New algorithm tackles complexity in non-stationary linear bandits

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new algorithm, Adjacent-BAI, to address the fixed-budget best-arm identification problem in non-stationary linear bandits. This problem involves identifying the arm with the highest cumulative reward under adversarial conditions and a fixed time budget. The new algorithm is based on an arm-set-dependent lower bound, which is more precise than previous complexity measures that were overly pessimistic. The proposed Adjacent-optimal design, a variation of the well-known XY-optimal design, is shown to achieve an error probability that tightly matches this new lower bound, establishing the arm-set-dependent complexity of the problem. AI

IMPACT Introduces a more precise theoretical framework for understanding and optimizing bandit algorithms in dynamic environments.

RANK_REASON The cluster contains an academic paper detailing a new algorithm and theoretical findings in a machine learning subfield. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New algorithm tackles complexity in non-stationary linear bandits

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The cluster contains an academic paper detailing a new algorithm and theoretical findings in a machine learning subfield. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Leo Maynard-Zhang, Zhihan Xiong, Kevin Jamieson, Maryam Fazel ·

    On the Complexity of Best-Arm Identification in Non-Stationary Linear Bandits

    arXiv:2603.10346v2 Announce Type: replace Abstract: We study the fixed-budget best-arm identification (BAI) problem in non-stationary linear bandits. Concretely, given a fixed time budget $T\in \mathbb{N}$, finite arm set $\mathcal{X} \subset \mathbb{R}^d$, and a potentially adve…

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