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ACTIVE LEARNING IN REGRESSION, WITH APPLICATION TO STOCHASTIC DYNAMIC PROGRAMMING

By Olivier Teytaud, Sylvain Gelly and Jérémie Mary

Abstract

Abstract: We study active learning as a derandomized form of sampling. We show that full derandomization is not suitable in a robust framework, propose partially derandomized samplings, and develop new active learning methods (i) in which expert knowledge is easy to integrate (ii) with a parameter for the exploration/exploitation dilemma (iii) less randomized than the full-random sampling (yet also not deterministic). Experiments are performed in the case of regression for value-function learning on a continuous domain. Our main results are (i) efficient partially derandomized point sets (ii) moderate-derandomization theorems (iii) experimental evidence of the importance of the frontier (iv) a new regression-specific user-friendly sampling tool lessrobust than blind samplers but that sometimes works very efficiently in large dimensions. All experiments can be reproduced by downloading the source code and running the provided command line.

Topics: Intelligent Control Systems and Optimization, Machine learning in control applications, Active learning
Year: 2009
OAI identifier: oai:CiteSeerX.psu:10.1.1.134.5015
Provided by: CiteSeerX
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