Non-stochastic bandit slate problems

Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010

Volumn , Issue , 2010, Pages

  Kale, Satyen ^a   Reyzin, Lev ^b   Schapirey, Robert E ^c  

^a YAHOO RESEARCH   (United States)

^b GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^c PRINCETON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BANDIT PROBLEMS; NON-STOCHASTIC; ONLINE ADVERTIZING; ONLINE NEWS; RELATIVE ENTROPY; SPECIFIC NATURE; UPDATE ALGORITHMS; WEIGHT UPDATE;

STOCHASTIC SYSTEMS;

EID: 85162455616     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. ABERNETHY, J., HAZAN, E., AND RAKHLIN, A. Competing in the dark: An efficient algorithm for bandit linear optimization. In COLT (2008), pp. 263-274.
2. AUER, P., CESA-BIANCHI, N., AND FISCHER, P. Finite-time analysis of the multiarmed bandit problem. Machine Learning 47, 2-3 (2002), 235-256.
3. AUER, P., CESA-BIANCHI, N., FREUND, Y., AND SCHAPIRE, R. E. The nonstochastic multiarmed bandit problem. SIAM J. Comput. 32, 1 (2002), 48-77.
4. AWERBUCH, B., AND KLEINBERG, R. Online linear optimization and adaptive routing. J. Comput. Syst. Sci. 74, 1 (2008), 97-114.
5. BARTLETT, P. L., DANI, V., HAYES, T. P., KAKADE, S., RAKHLIN, A., AND TEWARI, A. High-probability regret bounds for bandit online linear optimization. In COLT (2008), pp. 335-342.
6. BREGMAN, L. The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming. USSR Comp. Mathematics and Mathematical Physics 7 (1967), 200-217.
7. BRUALDI, R. A., AND LEE, G. M. On the truncated assignment polytope. Linear Algebra and its Applications 19 (1978), 33-62.
8. CENSOR, Y., AND ZENIOS, S. Parallel optimization. Oxford University Press, 1997.
9. CESA-BIANCHI, N., AND LUGOSI, G. Combinatorial bandits. In COLT (2009).
10. GYÖRGY, A., LINDER, T., LUGOSI, G., AND OTTUCSÁ K, G. The on-line shortest path problem under partial monitoring. Journal of Machine Learning Research 8 (2007), 2369-2403.
11. HAZAN, E., AND KALE, S. Better algorithms for benign bandits. In SODA (2009), pp. 38-47.
12. HELMBOLD, D. P., AND WARMUTH, M. K. Learning permutations with exponential weights. In COLT (2007), pp. 469-483.
13. HERBSTER, M., AND WARMUTH, M. K. Tracking the best linear predictor. Journal of Machine Learning Research 1 (2001), 281-309.
14. KOOLEN, W. M., WARMUTH, M. K., AND KIVINEN, J. Hedging structured concepts. In COLT (2010).
15. LAI, T., AND ROBBINS, H. Asymptotically efficient adaptive allocation rules. Advances in Applied Mathematics 6 (1985), 4-22.
16. MENDELSOHN, N. S., AND DULMAGE, A. L. The convex hull of sub-permutation matrices. Proceedings of the American Mathematical Society 9, 2 (Apr 1958), 253-254.
17. UCHIYA, T., NAKAMURA, A., AND KUDO, M. Algorithms for adversarial bandit problems with multiple plays. In ALT (2010), pp. 375-389.
18. WARMUTH, M. K., AND KUZMIN, D. Randomized PCA algorithms with regret bounds that are logarithmic in the dimension. In In Proc. of NIPS (2006).