Best arm identification in multi-armed bandits

COLT 2010 - The 23rd Conference on Learning Theory

Volumn , Issue , 2010, Pages 41-53

  Audibert, Jean Yves ^a,b   Bubeck, Sébastien ^c   Munos, Rémi ^c  

^a UNIVERSITÉ PARIS EST   (France)

^b ECOLE NORMALE SUPÉRIEURE   (France)

^c INRIA   (France)

Author keywords

[No Author keywords available]

Indexed keywords

MULTI ARMED BANDIT; NUMBER OF SAMPLES; UNOBSERVABLE;

OPTIMIZATION;

ALGORITHMS;

EID: 84864970677     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

1. P. Auer, N. Cesa-Bianchi, and P. Fischer. Finite-time analysis of the multiarmed bandit problem. Machine Learning Journal, 47(2-3):235-256, 2002.
2. S. Bubeck, R. Munos, and G. Stoltz. Pure exploration in multi-armed bandits problems. In Proc. of the 20th International Conference on Algorithmic Learning Theory, 2009.
3. C. Domingo, R. Gavaldà, and O. Watanabe. Adaptive sampling methods for scaling up knowledge discovery algorithms. Data Mining and Knowledge Discovery, 6(2):131-152, 2002.
4. E. Even-Dar, S. Mannor, and Y. Mansour. Action elimination and stopping conditions for the multi-armed bandit and reinforcement learning problems. The Journal of Machine Learning Research, 7:1079-1105, 2006.
5. O. Maron and A. W. Moore. Hoeffding races: Accelerating model selection search for classification and function approximation. In NIPS, pages 59-66, 1993.
6. V. Mnih, Cs. Szepesvári, and J.-Y. Audibert. Empirical bernstein stopping. In ICML, Volume 307, pages 672-679, 2008.
7. H. Robbins. Some aspects of the sequential design of experiments. Bulletin of the American Mathematics Society, 58:527-535, 1952.