On Bayesian upper confidence bounds for bandit problems

Journal of Machine Learning Research

Volumn 22, Issue , 2012, Pages 592-600

  Kaufmann, Emilie ^a   Cappé, Olivier ^a   Garivier, Aurélien ^a  

^a INSTITUT TELECOM   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; STOCHASTIC SYSTEMS;

ASYMPTOTIC OPTIMALITY; BAYESIAN APPROACHES; MEASURE OF PERFORMANCE; MULTI ARMED BANDIT; POSTERIOR DISTRIBUTIONS; PRIOR DISTRIBUTION; SPARSITY CONSTRAINTS; UPPER CONFIDENCE BOUND;

PROBABILITY;

EID: 84954519509     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (18)

1. P. Auer, N. Cesa-Bianchi, P. Fischer. Finite-time analysis of the multiarmed bandit problem Machine Learning 47, 235-256, 2002.
2. J-Y Audibert, S. Bubeck. Regret Bounds and Minimax Policies under Partial Monitoring Journal of Machine Learning Research, 2010.
3. A.N. Burnetas and M.N. Katehakis. Optimal adaptive policies for sequential allocation problems. in Advances in Applied Mathematics, 17 (2): 122-142, 1996.
4. V. Dani, T.P. Hayes, S.M. Kakade. Stochastic linear optimization under bandit feedback. In Conference On Learning Theory COLT 2008.
5. A. Garivier, O. Cappé. The KL-UCB algorithm for bounded stochastic bandits and beyond In Conference On Learning Theory COLT , 2011.
6. E. Frostig, G. Weiss, Four proofs of Gittins' multiarmed bandit theorem Preprint, 1999.
7. J.C. Gittins. Bandit processes and dynamic allocation indices. In Journal of the Royal Statistical Society Series B, 41 (2): 148-177, 1979.
8. J. Gittins, K. Glazebrook and R. Weber. Multi-armed bandit allocation indices (2nd Edition) Wiley, 2011.
9. O.C. Granmo. Solving Two-Armed Bernoulli Bandit Problems Using a Bayesian Learning Automaton in International Journal of Intelligent Computing and Cybernetics (IJICC) 3 (2): 207-234, 2010.
10. J. Honda and A. Takemura. An asymptotically optimal bandit algorithm for bounded support models. In T. Kalai and M. Mohri, editors, Conference On Learning Theory COLT, 2010.
11. T.L. Lai. Adaptive treatment allocation and the multi-armed bandit problem. In Annals of Statistics 15 (3): 1091-1114, 1987.
12. T.L. Lai, H. Robbins. Asymptotically efficient adaptive allocation rules. In Advances in Applied Mathematics 6 (1): 4-22, 1985.
13. J. Niño-Mora. Computing a classic index for Finite- Horizon bandits Journal on Computing 23 (2): 254-267, 2011
14. N.G Pavlidis, D.K. Tasoulis and D.J. Hand. Simulation studies of multi-armed bandits with covariates In Proc. 10th International Conference on Computer Modelling, Cambridge, UK, 2008.
15. O. Maillard, R. Munos, G. Stoltz. A finite-time analysis of Multi-armed bandits problems with Kullback- Leibler Divergence In Conference On Learning Theory COLT , 2011.
16. P. Rusmevichientong, J.N. Tsitsiklis. linearly Parameterized Bandits In Mathematics of Operations Research 32 (2): 395-411, 2010.
17. N. Srinivas, A. Krause, S. Kakade, and M. Seeger. Gaussian process optimization in the bandit setting: No regret and experimental design. In International Conference on Machine Learning ICML 10, 2010.
18. W.R. Thompson. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. In Biometrika 25: 285-294, 1933.