The K-armed dueling bandits problem

COLT 2009 - The 22nd Conference on Learning Theory

Volumn , Issue , 2009, Pages

  Yue, Yisong ^a   Broder, Josef ^b   Kleinberg, Robert ^a   Joachims, Thorsten ^a  

^a Department of Computer Science and School of Operations Research and Information Engineering   (United States)

^b Department of Obstetrics Gynecology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY FEEDBACK; CONSTANT FACTORS; CONVENTIONAL APPROACH; OPTIMAL REGRET; PAIR-WISE COMPARISON; PRODUCT ATTRACTIVENESS;

EID: 84898077397     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. Peter Auer, Nicolò Cesa-Bianchi, and Paul Fischer. Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47(2): 235-256, 2002.
2. Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, and Robert Schapire. The nonstochastic multiarmed bandit problem. SIAM Journal on Computing, 32(1): 48-77, 2002.
3. Micah Adler, Peter Gemmell, Mor Harchol-Balter, Richard Karp, and Claire Kenyon. Selection in the presence of noise: The design of playoff systems. In ACM-SIAM Symposium on Discrete Algorithms (SODA), 1994.
4. Nir Ailon and Mehryar Mohri. An efficient reduction of ranking to classification. In Conference on Learning Theory (COLT), 2008.
5. Peter Auer. Using confidence bounds for exploitation-exploration trade. Journal of Machine Learning Research (JMLR), 3: 397-422, 2003.
6. Maria-Florina Balcan, Nikhil Bansal, Alina Beygelzimer, Don Coppersmith, John Langford, and Gregory Sorkin. Robust reductions from ranking to classification. In Conference on Learning Theory (COLT), 2007.
7. Michael Ben-Or and Avinatan Hassidim. The bayesian learner is optimal for noisy binary search (and pretty good for quantum as well). In IEEE Symposium on Foundations of Computer Science (FOCS), 2008.
8. Nicolò Cesa-Bianchi, Gábor Lugosi, and Gilles Stoltz. Regret minimization under partial monitoring. Mathematics of Operations Research, 31(3): 562-580, 2006.
9. William Cohen, Robert Schapire, and Yoram Singer. Learning to order things. Journal of Artificial Intelligence Research (JAIR), 10: 243-270, 1999.
10. Thomas M. Cover and Joy A. Thomas. Elements of Information Theory. J. Wiley, 1999.
11. Eyal Even-Dar, Shie Mannor, and Yishay Mansour. Action elimination and stopping conditions for the multi-armed bandit and reinforcement learning problems. Journal of Machine Learning Research (JMLR), 7: 1079-1105, 2006.
12. Yoav Freund, Raj Iyer, Robert Schapire, and Yoram Singer. An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research (JMLR), 4: 933-969, 2003.
13. Uriel Feige, Prabhakar Raghavan, David Peleg, and Eli Upfal. Computing with noisy information. SIAM Journal on Computing, 23(5), 1994.
14. Ralf Herbrich, Thore Graepel, and Klaus Obermayer. Support vector learning for ordinal regression. In International Conference on Artificial Neural Networks (ICANN), 1999.
15. Wassily Hoeffding. Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association, 58: 13-30, 1963.
16. Thorsten Joachims. A support vector method for multivariate performance measures. In International Conference on Machine Learning (ICML), 2005.
17. Richard M Karp and Robert Kleinberg. Noisy binary search and its applications. In ACM-SIAM Symposium on Discrete Algorithms (SODA), 2007.
18. Robert Kleinberg, Alexandru Niculescu-Mizil, and Yogeshwer Sharma. Regret bounds for sleeping experts and bandits. In Conference on Learning Theory (COLT), 2008.
19. John Langford. How do we get weak action dependence for learning with partial observations? http://hunch.net/?p=421, September 2008. Blog entry at Machine Learning (Theory).
20. T. L. Lai and Herbert Robbins. Asymptotically efficient adaptive allocation rules. Advances in Applied Mathematics, 6: 4-22, 1985.
21. Phil Long and Rocco Servedio. Boosting the area under the roc curve. In Proceedings of Neural Information Processing Systems (NIPS), 2007.
22. John Langford and Tong Zhang. The epoch-greedy algorithm for contextual multi-armed bandits. In Proceedings of Neural Information Processing Systems (NIPS), 2007.
23. Rajeev Motwani and Prabhakar Raghavan. Randomized Algorithms. Cambridge University Press, 1995.
24. Shie Mannor and John N. Tsitsiklis. The sample complexity of exploration in the multi-armed bandit problem. Journal of Machine Learning Research (JMLR), 5: 623-648, 2004.
25. Sandeep Pandey, Deepak Agarwal, Deepayan Chakrabarti, and Vanja Josifovski. Bandits for taxonomies: A model-based approach. In SIAM Conference on Data Mining (SDM), 2007.
26. Filip Radlinski, Madhu Kurup, and Thorsten Joachims. How does clickthrough data reflect retrieval quality? In ACM Conference on Information and Knowledge Management (CIKM), 2008.
27. Herbert Robbins. Some Aspects of the Sequential Design of Experiments. Bull. Amer. Math. Soc., 58: 527-535, 1952.
28. Yisong Yue and Thorsten Joachims. Interactively optimizing information retrieval systems as a dueling bandits problem. In International Conference on Machine Learning (ICML), 2009.