Online learning with switching costs and other adaptive adversaries

Advances in Neural Information Processing Systems

Volumn , Issue , 2013, Pages

  Cesa Bianchi, Nicolò ^a   Dekel, Ofer ^b   Shamir, Ohad ^b  

^a UNIVERSITY OF MILAN   (Italy)

^b MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COSTS;

ADAPTIVE ADVERSARY; BANDIT FEEDBACKS; BOUNDED MEMORY; FULL INFORMATIONS; ONLINE LEARNING; PREDICTION WITH EXPERT ADVICE; STOCHASTIC ADVERSARY; SWITCHING COSTS;

SWITCHING;

EID: 84894413813     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. J. Abernethy and A. Rakhlin. Beating the adaptive bandit with high probability. In COLT, 2009.
2. R. Agrawal, M.V. Hedge, and D. Teneketzis. Asymptotically efficient adaptive allocation rules for the multiarmed bandit problem with switching cost. IEEE Transactions on Automatic Control, 33(10):899-906, 1988.
3. R. Arora, O. Dekel, and A. Tewari. Online bandit learning against an adaptive adversary: from regret to policy regret. In Proceedings of the Twenty-Ninth International Conference on Machine Learning, 2012.
4. P. Auer, N. Cesa-Bianchi, Y. Freund, and R. Schapire. The nonstochastic multiarmed bandit problem. SIAM Journal on Computing, 32(1):48-77, 2002.
5. A. Borodin and R. El-Yaniv. Online computation and competitive analysis. Cambridge University Press, 1998.
6. S. Bubeck, R. Munos, G. Stoltz, and C. Szepesvári. X-armed bandits. Journal of Machine Learning Research, 12:1655-1695, 2011.
7. N. Cesa-Bianchi, C. Gentile, and Y. Mansour. Regret minimization for reserve prices in second-price auctions. In Proceedings of the ACM-SIAM Symposium on Discrete Algorithms (SODA13), 2013.
8. N. Cesa-Bianchi and G. Lugosi. Prediction, learning, and games. Cambridge University Press, 2006.
9. N. Cesa-Bianchi, Y. Mansour, and G. Stoltz. Improved second-order bounds for prediction with expert advice. Machine Learning, 66(2/3):321-352, 2007.
10. V. Dani and T. P. Hayes. Robbing the bandit: Less regret in online geometric optimization against an adaptive adversary. In Proceedings of the Seventeenth Annual ACM-SIAM Symposium on Discrete Algorithms, 2006.
11. Y. Freund and R.E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of computer and System Sciences, 55(1):119-139, 1997.
12. A. Gyorgy and G. Neu. Near-optimal rates for limited-delay universal lossy source coding. In IEEE International Symposium on Information Theory, pages 2218-2222, 2011.
13. T. Jun. A survey on the bandit problem with switching costs. De Economist, 152:513-541, 2004.
14. A. Kalai and S. Vempala. Efficient algorithms for online decision problems. Journal of Computer and System Sciences, 71:291-307, 2005.
15. N. Littlestone and M.K. Warmuth. The weighted majority algorithm. Information and Computation, 108:212-261, 1994.
16. O. Maillard and R. Munos. Adaptive bandits: Towards the best history-dependent strategy. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.
17. H. B. McMahan and A. Blum. Online geometric optimization in the bandit setting against an adaptive adversary. In Proceedings of the Seventeenth Annual Conference on Learning Theory, 2004.
18. N. Merhav, E. Ordentlich, G. Seroussi, and M.J. Weinberger. Sequential strategies for loss functions with memory. IEEE Transactions on Information Theory, 48(7):1947-1958, 2002.
19. C. Mesterharm. Online learning with delayed label feedback. In Proceedings of the Sixteenth International Conference on Algorithmic Learning Theory, 2005.
20. R. Ortner. Online regret bounds for Markov decision processes with deterministic transitions. Theoretical Computer Science, 411(29-30):2684-2695, 2010.
21. O. Shamir. On the complexity of bandit and derivative-free stochastic convex optimization. CoRR, abs/1209.2388, 2012.