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New BAI with Minimal Regret Problem Introduced in Machine Learning

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced a new problem called best arm identification (BAI) with minimal regret, which combines the objectives of identifying the best arm in a multi-armed bandit problem with minimizing cumulative regret. The study focuses on single-parameter exponential families and establishes a lower bound on expected cumulative regret using information-theoretic techniques. Additionally, an impossibility result highlights the trade-off between regret and sample complexity in fixed-confidence BAI, while the proposed Double KL-UCB algorithm demonstrates asymptotic optimality as confidence levels decrease. AI

RANK_REASON The cluster contains an academic paper detailing a new problem formulation and algorithm in machine learning. [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 BAI with Minimal Regret Problem Introduced in Machine Learning

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

  1. arXiv cs.LG TIER_1 English(EN) · Junwen Yang, Vincent Y. F. Tan, Tianyuan Jin ·

    Best Arm Identification with Minimal Regret

    arXiv:2409.18909v2 Announce Type: replace Abstract: Motivated by real-world applications that necessitate responsible experimentation, we introduce the problem of best arm identification (BAI) with minimal regret. This variant of the multi-armed bandit problem elegantly amalgamat…

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