# Optimal Best Arm Identification with Fixed Confidence

4 SEQUEL - Sequential Learning
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : We give a complete characterization of the complexity of best-arm identification in one-parameter bandit problems. We prove a new, tight lower bound on the sample complexity. We propose the Track-and-Stop' strategy, which we prove to be asymptotically optimal. It consists in a new sampling rule (which tracks the optimal proportions of arm draws highlighted by the lower bound) and in a stopping rule named after Chernoff, for which we give a new analysis.
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Conference papers
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Cited literature [29 references]

https://hal.archives-ouvertes.fr/hal-01273838
Contributor : Aurélien Garivier Connect in order to contact the contributor
Submitted on : Wednesday, June 1, 2016 - 2:25:34 PM
Last modification on : Wednesday, October 27, 2021 - 12:59:41 PM
Long-term archiving on: : Friday, September 2, 2016 - 10:27:56 AM

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MDLBAI.pdf
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### Identifiers

• HAL Id : hal-01273838, version 2
• ARXIV : 1602.04589

### Citation

Aurélien Garivier, Emilie Kaufmann. Optimal Best Arm Identification with Fixed Confidence. 29th Annual Conference on Learning Theory (COLT), Jun 2016, New York, United States. ⟨hal-01273838v2⟩

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