We develop a new tool for data-dependent analysis of the exploration-exploitation trade-off in learning under limited feedback. Our tool is based on two main ingredients. The first ingredient is a new concentration inequality that makes it possible to control the concentration of weighted averages of multiple (possibly uncountably many) simultaneously evolving and interdependent martingales. The second ingredient is an application of this inequality to the exploration-exploitation trade-off via importance weighted sampling. We apply the new tool to the stochastic multiarmed bandit problem, however, the main importance of this paper is the development and understanding of the new tool rather than improvement of existing algorithms for stochastic multiarmed bandits. In the follow-up work we demonstrate that the new tool can improve over state-of-the-art in structurally richer problems, such as stochastic multiarmed bandits with side information.
PAC-Bayes-Bernstein inequality for martingales and its application to multiarmed bandits / Y. Seldin, N. Cesa-Bianchi, P. Auer, F. Laviolette, J. Shawe-Taylor - In: Proceedings of the Workshop on on-line trading of exploration and exploitation 2 : july 2, 2011, Bellevue, Washington, USA[s.l] : Microtome, 2012. (( convegno On-line Trading of Exploration and Exploitation tenutosi a Bellevue, Washington, USA nel 2011.
|Titolo:||PAC-Bayes-Bernstein inequality for martingales and its application to multiarmed bandits|
CESA BIANCHI, NICOLO' ANTONIO (Secondo)
|Parole Chiave:||PAC-Bayesian analysis ; Bernstein's inequality ; Martingales ; Multiarmed bandits ; Model order selection ; Exploration-exploitation trade-off|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
|Progetto:||Pattern Analysis, Statistical Modelling and Computational Learning 2|
|Data di pubblicazione:||2012|
|Tipologia:||Book Part (author)|
|Appare nelle tipologie:||03 - Contributo in volume|