Bayesian Active Learning for Classification and Preference Learning

TL;DR

This paper introduces a minimal-approximation active learning method based on information gain for Gaussian Process Classifiers and extends it to preference learning, achieving competitive performance with lower computational costs.

Contribution

It proposes a new active learning approach using predictive entropies for GPC that minimizes approximations and extends to preference learning.

Findings

Favourable experimental performance compared to popular algorithms

Lower computational complexity than decision theoretic approaches

Effective extension to Gaussian Process preference learning

Abstract

Information theoretic active learning has been widely studied for probabilistic models. For simple regression an optimal myopic policy is easily tractable. However, for other tasks and with more complex models, such as classification with nonparametric models, the optimal solution is harder to compute. Current approaches make approximations to achieve tractability. We propose an approach that expresses information gain in terms of predictive entropies, and apply this method to the Gaussian Process Classifier (GPC). Our approach makes minimal approximations to the full information theoretic objective. Our experimental performance compares favourably to many popular active learning algorithms, and has equal or lower computational complexity. We compare well to decision theoretic approaches also, which are privy to more information and require much more computational time. Secondly, by developing further a reformulation of binary preference learning to a classification problem, we extend our algorithm to Gaussian Process preference learning.