Gaussian process modelling of dependencies in multi-armed bandit problems

Proceedings of the 10th International Symposium on Operational Research in Slovenia, SOR 2009

Volumn , Issue , 2009, Pages 77-84

  Dorard, Louis ^a   Glowacka, Dorota ^a   Shawe Taylor, John ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

Artificial intelligence; Bandit problems; Gaussian processes; Kernel methods; Learning and optimisation; Machine learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; COVARIANCE MATRIX; GAUSSIAN DISTRIBUTION; LEARNING SYSTEMS; STATISTICS;

BANDIT PROBLEMS; GAUSSIAN PROCESS; GAUSSIAN PROCESSES; KERNEL METHODS; MULTI-ARMED BANDIT PROBLEM; OPTIMISATIONS;

GAUSSIAN NOISE (ELECTRONIC);

EID: 84860257669     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

1. Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer. Finite-time analysis of the multiarmed bandit problem. Machine Learning, 2002.
2. Sebastien Bubeck, Remi Munos, Gilles Stoltz, and Csaba Szepesvari. Online optimization in xarmed bandits. In Proceedings of NIPS, 2008.
3. Sylvain Gelly and Yizao Wang. Exploration exploitation in go: Uct for monte-carlo go. In Proceedings of NIPS, 2006.
4. Robert Kleinberg, Aleksandrs Slivkins, and Eli Upfal. Multi-armed bandits in metric spaces. In Proceedings of STOC, 2008.
5. Levente Kocsis and Csaba Szepesvari. Bandit based monte-carlo planning. In Proceedings of ECML, 2006.
6. Sandeep Pandey, Deepayan Chakrabarti, and Deepak Agarwal. Multi-armed bandit problems with dependent arms. In Proceedings of ICML, 2007.
7. Carl Edward Rasmussen and Christopher K.I.Williams. Gaussian Processes for Machine Learning. The MIT Press, 2006.