Graphical models for bandit problems

Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011

Volumn , Issue , 2011, Pages 1-10

  Amin, Kareem ^a   Kearns, Michael ^a   Syed, Umar ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; GRAPH ALGORITHMS; PROBABILITY;

ACTION SPACES; BANDIT PROBLEMS; CONTEXT SPACES; GRAPHICAL MODEL; MULTI-ARMED BANDIT PROBLEM; RUNNING TIME; TREE-WIDTH;

GRAPHIC METHODS;

EID: 80053151768     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, and Robert E. Schapire. The nonstochastic multiarmed bandit problem. SIAM Journal of Computing, 32 (1):48-77, 2002.
2. John Langford and Tong Zhang. The epoch-greedy algorithm for contextual multi-armed bandits. In Advances in Neural Information Processing Systems 20, 2007.
3. Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, and Robert E. Schapire. Contextual ban-dit algorithms with supervised learning guarantees. In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, 2011.
4. Naoki Abe, Alan W. Biermann, and Philip M. Long.Reinforcement learning with immediate rewards and linear hypotheses. Algorithmica, 37:263-293, 2003.
5. Peter Auer. Using confidence bounds for exploitation-exploration trade-offs. Journal of Machine Learning Research, 3:397-422, 2002.
6. Lihong Li, Wei Chu, John Langford, and Robert E. Schapire. A contextual-bandit approach to person-alized news article recommendation. In roceedings of the 19th International World Wide Web Conference, 2010.
7. Aleksandrs Slivkins. Contextual bandits with simi-larity information. In Proceedings of the 24th An-nual Conference on Computational Learning Theory, 2011.
8. Tyler Lu, David Pal, and Martin Pal. Contextual multi-armed bandits. In Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, 2010.
9. Lihong Li, Michael L Littman, and Thomas J Walsh. Knows what it knows: A framework for self- aware learning. In Proceedings of the 27th International Conference on Machine Learning, 2008.
10. Francisco Barahona. On the computational complexity of Ising spin glass models. Journal of Physics A: Mathematical, Nuclear and General, 15:3241-3253, 1982.
11. Alexander Strehl andMichael L Littman. Online linear regression and its application to model-based reinforcement learning. In Advances in Neural Information Processing Systems 20, 2007.
12. Nicolò Cesa-Bianchi, Claudio Gentile, and Francesco Orabona. Robust bounds for classification via selective sampling. In Proceedings of the 28th International Conference on Machine Learning, 2009.
13. Jacob Abernathy and Alexander Rakhlin. An efficient bandit algorithm for t1/2-regret in online multiclass prediction? In Proceedings of the 22nd Annual Conference on Computational Learning Theory, 2009.