An efficient bandit algorithm for √T-regret in online multiclass prediction?

COLT 2009 - The 22nd Conference on Learning Theory

Volumn , Issue , 2009, Pages

  Abernethy, Jacob ^a   Rakhlin, Alexander ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84898768231     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

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3. N. Cesa-Bianchi, G. Lugosi, and G. Stoltz. Regret minimization under partial monitoring. MATHEMATICS OF OPERATIONS RESEARCH, 31(3): 562, 2006.
4. K. Crammer and Y. Singer. Ultraconservative online algorithms for multiclass problems. The Journal of Machine Learning Research, 3: 951-991, 2003.
5. 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 algorithm, pages 937-943. ACM New York, NY, USA, 2006.
6. V. Dani, T. Hayes, and S. M. Kakade. The price of bandit information for online optimization. Advances in Neural Information Processing Systems, 20, 2008.
7. M. Fink, S. Shalev-Shwartz, Y. Singer, and S. Ullman. Online multiclass learning by interclass hypothesis sharing. In Proceedings of the 23rd international conference on Machine learning, pages 313-320. ACM New York, NY, USA, 2006.
8. S. M. Kakade, S. Shalev-Shwartz, and A. Tewari. Efficient bandit algorithms for online multiclass prediction. In Proceedings of the 25th international conference on Machine learning, pages 440-447. ACM New York, NY, USA, 2008.