Multi-armed Bandit Algorithms and Empirical Evaluation-Reference-Cited by-同舟云学术

Multi-armed Bandit Algorithms and Empirical Evaluation

Published:2005 Issue: Volume: Page:437-448
ISSN:0302-9743
Container-title:Machine Learning: ECML 2005
language:
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Author:

Vermorel Joannès,Mohri Mehryar

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/11564096_42

Reference25 articles.

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2. Auer, P., Cesa-Bianchi, N., Freund, Y., Schapire, R.E.: Gambling in a Rigged Casino: the Adversarial Multi-Armed Bandit Problem. In: Proceedings of the 36th Annual Symposium on Foundations of Computer Science (FOCS 1995), pp. 322–331. IEEE Computer Society Press, Los Alamitos (1995)

3. Auer, P., Cesa-Bianchi, N., Freund, Y., Schapire, R.E.: The nonstochastic multiarmed bandit problem. SIAM Journal on Computing 32(1), 48–77 (2002)

4. Awerbuch, B., Kleinberg, R.: Adaptive Routing with End-to-End feedback: Distributed Learning and Geometric Approaches. In: Proceedings of the 36th ACM Symposium on Theory of Computing (STOC 2004), pp. 45–53 (2004)

5. Cesa-Bianchi, N., Fischer, P.: Finite-Time Regret Bounds for the Multiarmed Bandit Problem. In: Proceedings of the 15th International Conference on Machine Learning (ICML 1998), pp. 100–108. Morgan Kaufmann, San Francisco (1998)

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