ACTIVE LEARNING IN REGRESSION, WITH APPLICATION TO
STOCHASTIC DYNAMIC PROGRAMMING
Olivier Teytaud, Sylvain Gelly and Jeremie Mary
In: ICINCO07, Angers(2007).
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 less-
robust 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.