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Observe that A := 1 t bΣt +λtId −1 tX s=1 (Σ +λtId) 1 2 (Σ +λtId)− 1 2exasqs = 1 t bΣt +λtId −1(Σ +λtId) 1 2 tX s=1 (Σ +λtId)− 1 2exas| {z } :=es qs := 1 t bΣt +λtId −1(Σ +λtId) 1 2 tX s=1 esqs · 2019

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Experimental Design for Semiparametric Bandits

stat.ML · 2025-06-16 · conditional · novelty 7.0

A new design and a sharper analysis of orthogonalized regression yield optimal regret, logarithmic regret under gaps, and the first PAC and best-arm identification guarantees for semiparametric bandits.

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  • Experimental Design for Semiparametric Bandits stat.ML · 2025-06-16 · conditional · none · ref 2

    A new design and a sharper analysis of orthogonalized regression yield optimal regret, logarithmic regret under gaps, and the first PAC and best-arm identification guarantees for semiparametric bandits.