Thompson Sampling: An Asymptotically Optimal Finite-Time Analysis

Author:

Kaufmann Emilie,Korda Nathaniel,Munos Rémi

Publisher

Springer Berlin Heidelberg

Reference14 articles.

1. Agrawal, S., Goyal, N.: Analysis of thompson sampling for the multi-armed bandit problem. In: Conference on Learning Theory, COLT (2012)

2. Audibert, J.-Y., Bubeck, S.: Regret bounds and minimax policies under partial monitoring. Journal of Machine Learning Research 11, 2785–2836 (2010)

3. Audibert, J.-Y., Munos, R., Szepesvári, C.: Exploration-exploitation trade-off using variance estimates in multi-armed bandits. Theoretical Computer Science 410(19), 1876–1902 (2009)

4. Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Machine Learning 47(2), 235–256 (2002)

5. Chapelle, O., Li, L.: An empirical evaluation of thompson sampling. In: NIPS (2011)

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