Foundations and Trends® in Machine Learning

Volume 7, issue 2-3

Theory of Disagreement-Based Active Learning

Active learning is a protocol for supervised machine learning, in which a learning algorithm sequentially requests the labels of selected data points from a large pool of unlabeled data. This contrasts with passive learning, where the labeled data are taken at random. The objective in active learn
Volume 7, issue 1

From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning

This work covers several aspects of the optimism in the face of uncertainty principle applied to large scale optimization problems under finite numerical budget. The initial motivation for the research reported here originated from the empirical success of the so-called Monte-Carlo Tree Search met