-
1
-
-
77951164997
-
Explore/exploit schemes for web content optimization
-
Agarwal, D., Chen, B., and Elango, P. Explore/exploit schemes for web content optimization. In Proc. Ninth IEEE International Conference on Data Mining (ICDM'2009), pp. 1-10, 2009.
-
(2009)
Proc. Ninth IEEE International Conference on Data Mining (ICDM'2009)
, pp. 1-10
-
-
Agarwal, D.1
Chen, B.2
Elango, P.3
-
2
-
-
84864970677
-
Best arm identification in multi-armed bandits
-
Audibert, J.Y., Bubeck, S., and Munos, R. Best arm identification in multi-armed bandits. In COLT, pp. 41-53, 2010.
-
(2010)
COLT
, pp. 41-53
-
-
Audibert, J.Y.1
Bubeck, S.2
Munos, R.3
-
3
-
-
77957337199
-
UCB revisited: Improved regret bounds for the stochastic multi-armed bandit problem
-
Auer, P. and Ortner, R. UCB revisited: Improved regret bounds for the stochastic multi-armed bandit problem. Periodica Mathematica Hungarica, 61(1-2):55-65, 2010.
-
(2010)
Periodica Mathematica Hungarica
, vol.61
, Issue.1-2
, pp. 55-65
-
-
Auer, P.1
Ortner, R.2
-
4
-
-
0036568025
-
Finite-time analysis of the multiarmed bandit problem
-
DOI 10.1023/A:1013689704352, Computational Learning Theory
-
Auer, P., Cesa-Bianchi, N., and Fischer, P. Finite-time analysis of the multiarmed bandit problem. Machine learning, 47(2):235-256, 2002. (Pubitemid 34126111)
-
(2002)
Machine Learning
, vol.47
, Issue.2-3
, pp. 235-256
-
-
Auer, P.1
Cesa-Bianchi, N.2
Fischer, P.3
-
5
-
-
77952070805
-
Pure exploration in multi-armed bandits problems
-
Springer
-
Bubeck, S., Munos, R., and Stoltz, G. Pure exploration in multi-armed bandits problems. In Algorithmic Learning Theory, pp. 23-37. Springer, 2009.
-
(2009)
Algorithmic Learning Theory
, pp. 23-37
-
-
Bubeck, S.1
Munos, R.2
Stoltz, G.3
-
6
-
-
84897498871
-
Multiple identifications in multi-armed bandits
-
Bubeck, S., Wang, T., and Viswanathan, N. Multiple identifications in multi-armed bandits. In Proceedings of the 30th International Conference on Machine Learning, 2013.
-
Proceedings of the 30th International Conference on Machine Learning, 2013
-
-
Bubeck, S.1
Wang, T.2
Viswanathan, N.3
-
7
-
-
84863406076
-
Mortal multi-armed bandits
-
Chakrabarti, D., Kumar, R., Radlinski, F., and Upfal, E. Mortal multi-armed bandits. In Proceedings of the 22nd Annual Conference on Neural Information Processing Systems (NIPS'2008), pp. 273-280, 2008.
-
(2008)
Proceedings of the 22nd Annual Conference on Neural Information Processing Systems (NIPS'2008)
, pp. 273-280
-
-
Chakrabarti, D.1
Kumar, R.2
Radlinski, F.3
Upfal, E.4
-
8
-
-
9444277556
-
PAC bounds for multi-armed bandit and markov decision processes
-
Springer
-
Even-Dar, E., Mannor, S., and Mansour, Y. PAC bounds for multi-armed bandit and markov decision processes. In COLT, pp. 193-209. Springer, 2002.
-
(2002)
COLT
, pp. 193-209
-
-
Even-Dar, E.1
Mannor, S.2
Mansour, Y.3
-
9
-
-
33745295134
-
Action elimination and stopping conditions for the multi-armed bandit and reinforcement learning problems
-
Even-Dar, E., Mannor, S., and Mansour, Y. Action elimination and stopping conditions for the multi-armed bandit and reinforcement learning problems. The Journal of Machine Learning Research, 7:1079-1105, 2006. (Pubitemid 43938989)
-
(2006)
Journal of Machine Learning Research
, vol.7
, pp. 1079-1105
-
-
Even-Bar, E.1
Mannor, S.2
Mansour, Y.3
-
10
-
-
85162482585
-
Multi-bandit best arm identification
-
Gabillon, V., Ghavamzadeh, M., Lazaric, A., and Bubeck, S. Multi-bandit best arm identification. In Advances in Neural Information Processing Systems 24, pp. 2222-2230. 2011.
-
(2011)
Advances in Neural Information Processing Systems 24
, pp. 2222-2230
-
-
Gabillon, V.1
Ghavamzadeh, M.2
Lazaric, A.3
Bubeck, S.4
-
11
-
-
84877730309
-
Best arm identification: A unified approach to fixed budget and fixed confidence
-
Gabillon, V., Ghavamzadeh, M., and Lazaric, A. Best arm identification: A unified approach to fixed budget and fixed confidence. In Advances in Neural Information Processing Systems 25, pp. 3221-3229, 2012.
-
(2012)
Advances in Neural Information Processing Systems 25
, pp. 3221-3229
-
-
Gabillon, V.1
Ghavamzadeh, M.2
Lazaric, A.3
-
13
-
-
84867131498
-
PAC subset selection in stochastic multi-armed bandits
-
Kalyanakrishnan, S., Tewari, A., Auer, P., and Stone, P. PAC subset selection in stochastic multi-armed bandits. In Proceedings of the 29th International Conference on Machine Learning, ICML 2012, 2012.
-
(2012)
Proceedings of the 29th International Conference on Machine Learning, ICML 2012
-
-
Kalyanakrishnan, S.1
Tewari, A.2
Auer, P.3
Stone, P.4
-
14
-
-
0002899547
-
Asymptotically efficient adaptive allocation rules
-
Lai, Tze Leung and Robbins, Herbert. Asymptotically efficient adaptive allocation rules. Advances in applied mathematics, 6(1):4-22, 1985.
-
(1985)
Advances in Applied Mathematics
, vol.6
, Issue.1
, pp. 4-22
-
-
Lai, T.L.1
Robbins, H.2
-
15
-
-
30044441333
-
The sample complexity of exploration in the multi-armed bandit problem
-
Mannor, S. and Tsitsiklis, J.N. The sample complexity of exploration in the multi-armed bandit problem. The Journal of Machine Learning Research, 5:623-648, 2004.
-
(2004)
The Journal of Machine Learning Research
, vol.5
, pp. 623-648
-
-
Mannor, S.1
Tsitsiklis, J.N.2
-
16
-
-
70049106076
-
Bandits for taxonomies: A model based approach
-
Pandey, S., Agarwal, D., Chakrabarti, D., and Josifovski, V. Bandits for taxonomies: A model based approach. In In Proceedings of the SIAM International Conference on Data Mining. SDM, 2007.
-
Proceedings of the SIAM International Conference on Data Mining. SDM, 2007
-
-
Pandey, S.1
Agarwal, D.2
Chakrabarti, D.3
Josifovski, V.4
-
17
-
-
56449088596
-
Learning diverse rankings with multi-armed bandits
-
ACM
-
Radlinski, F., Kleinberg, R., and Joachims, T. Learning diverse rankings with multi-armed bandits. In Proceedings of the 25th International Conference on Machine Learning, pp. 784-791. ACM, 2008.
-
(2008)
Proceedings of the 25th International Conference on Machine Learning
, pp. 784-791
-
-
Radlinski, F.1
Kleinberg, R.2
Joachims, T.3
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