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New algorithm Exp-KL-MS tackles multi-armed bandit challenges

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new algorithm called Exponential-Kullback Leibler Maillard Sampling (Exp-KL-MS) to address the multi-armed bandit problem. This algorithm aims to simultaneously satisfy multiple performance criteria, including Asymptotic Optimality, Minimax Optimality with a $\sqrt{\ln (K)}$ factor, Sub-UCB, and a variance-adaptive regret bound. The Exp-KL-MS algorithm utilizes a Minimum Empirical Divergence framework, where the exploration probability is determined by an inverse temperature function and the Kullback-Leibler divergence between empirical means of arms. AI

IMPACT Introduces a novel algorithm for optimizing decision-making in bandit problems, potentially improving efficiency in reinforcement learning scenarios.

RANK_REASON The cluster contains an academic paper detailing a new algorithm for a machine learning problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

New algorithm Exp-KL-MS tackles multi-armed bandit challenges

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The cluster contains an academic paper detailing a new algorithm for a machine learning problem. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Hao Qin, Kwang-Sung Jun, Chicheng Zhang ·

    Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leibler Maillard Sampling

    arXiv:2502.14379v3 Announce Type: replace Abstract: We study the problem of $K$-armed bandits with reward distributions belonging to a one-parameter exponential distribution family. In the literature, several criteria have been proposed to evaluate the performance of such algorit…

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