Combinatorial bandits

COLT 2009 - The 22nd Conference on Learning Theory

Volumn , Issue , 2009, Pages

  Cesa Bianchi, Nicolò ^a   Lugosi, Gabor ^b  

^a UNIVERSITY OF MILAN   (Italy)

^b UNIVERSITAT POMPEU FABRA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY VECTORS; COMPUTATIONALLY EFFICIENT; REGRET BOUNDS; SEQUENTIAL PREDICTION; TIME HORIZONS; TIME INSTANCES; VECTOR COMPONENTS;

VECTORS;

EID: 84898061746     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. J. Abernethy, E. Hazan, and A. Rakhlin. Competing in the dark: An efficient algorithm for bandit linear optimization. In Proceedings of the 21st Annual Conference on Learning Theory (COLT 2008), pages 263-274. Omnipress, 2008.
2. P. Auer, N. Cesa-Bianchi, Y. Freund, and R.E. Schapire. The nonstochastic multiarmed bandit problem. SIAM Journal on Computing, 32: 48-77, 2002.
3. B. Awerbuch and R.D. Kleinberg. Adaptive routing with end-to-end feedback: distributed learning and geometric approaches. In Proceedings of the 36th Annual ACM Symposium on Theory of Computing (STOC 2004), pages 45-53. ACM Press, 2004.
4. P. Bartlett, V. Dani, T. Hayes, S.M. Kakade, A. Rakhlin, and A. Tewari. High-probability regret bounds for bandit online linear optimization. In Proceedings of the 21st Annual Conference on Learning Theory (COLT 2008), pages 335-342. Omnipress, 2008.
5. R. Burton and R. Pemantle. Local characteristics, entropy and limit theorems for spanning trees and domino tilings via transfer-impedances. The Annals of Probability, 21: 1329-1371, 1993.
6. N. Cesa-Bianchi and G. Lugosi. Prediction, Learning, and Games. Cambridge Univ. Press. New York, 2006.
7. V. Dani, T. Hayes, and S.M. Kakade. The price of bandit information for online optimization. In NIPS 20, pages 345-352. MIT Press, 2008.
8. V. Dani and T.P. Hayes. Robbing the bandit: less regret in online geometric optimization against an adaptive adversary. In Proceedings of the 17th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2006), pages 937-943. ACM/SIAM, 2006.
9. P.G. Doyle and J.L. Snell. Random walks and electric networks, Volume 22 of Carus Mathematical Monographs. Mathematical Association of America. Washington, DC, 1984.
10. A. György, T. Linder, G. Lugosi, and Gy. Ottucsák. The on-line shortest path problem under partial monitoring. Journal of Machine Learning Research, 8: 2369-2403, 2007.
11. D.P. Helmbold and M.K. Warmuth. Learning permutations with exponential weights. In Proceedings of the 20th Annual Conference on Learning Theory (COLT 2007), pages 469-483. Springer, 2007.
12. M. Jerrum and A. Sinclair. Polynomial-time approximation algorithms for the Ising model. SIAM Journal on Computing, 22: 1087-1116, 1993.
13. M. Jerrum, A. Sinclair, and E. Vigoda. A polynomial-time approximation algorithm for the permanent of a matrix with nonnegative entries. Journal of the ACM, 51: 671-697, 2004.
14. A. Kalai and S. Vempala. Efficient algorithms for the online decision problem. In Proceedings of the 16th Annual Conference on Learning Theory (COLT 2003), pages 26-40. Springer, 2003.
15. R. Lyons and Y. Peres. Probability on Trees and Networks. To be published, 2008.
16. H.B. McMahan and A. Blum. Online geometric optimization in the bandit setting against an adaptive adversary. In Proceedings of the 17th Annual Conference on Learning Theory (COLT 2004), pages 109-123. Springer, 2004.
17. J.G. Propp and D.B. Wilson. How to get a perfectly random sample from a generic Markov chain and generate a random spanning tree of a directed graph. Journal of Algorithms, 27: 170-217, 1998.
18. D. Randall and D.B. Wilson. Sampling spin configurations of an Ising system. In Proceedings of the 10th ACM-SIAM Symposium on Discrete Algorithms (SODA 1999), pages 959-960. ACM/SIAM, 1999.
19. E. Takimoto and M. Warmuth. Path kernels and multiplicative updates. Journal of Machine Learning Research, 4(5): 773-818, 2004.