Skip to Main content Skip to Navigation
New interface
Conference papers

Upper Confidence Trees and Billiards for Optimal Active Learning

Philippe Rolet 1, 2 Michèle Sebag 1, 2 Olivier Teytaud 1, 2 
2 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : This paper focuses on Active Learning (AL) with bounded compu- tational resources. AL is formalized as a finite horizon Reinforcement Learning problem, and tackled as a single-player game. An approximate optimal AL strat- egy based on tree-structured multi-armed bandit algorithms and billiard-based sampling is presented together with a proof of principle of the approach.
Document type :
Conference papers
Complete list of metadata

Cited literature [19 references]  Display  Hide  Download
Contributor : Olivier Teytaud Connect in order to contact the contributor
Submitted on : Saturday, March 21, 2009 - 9:58:28 AM
Last modification on : Tuesday, October 25, 2022 - 4:17:57 PM
Long-term archiving on: : Friday, October 12, 2012 - 2:05:35 PM


Files produced by the author(s)


  • HAL Id : inria-00369787, version 1


Philippe Rolet, Michèle Sebag, Olivier Teytaud. Upper Confidence Trees and Billiards for Optimal Active Learning. CAP09, 2009, Hammamet, Tunisie, Tunisia. ⟨inria-00369787⟩



Record views


Files downloads