Batch Active Learning via Coordinated Matching

Abstract

We propose a novel batch active learning method that leverages the availability of high-quality and efficient sequential active-learning policies by approximating their behavior when applied for k steps. Specifically, our algorithm uses Monte-Carlo simulation to estimate the distribution of unlabeled examples selected by a sequential policy over k steps. The algorithm then selects k examples that best matches this distribution, leading to a combinatorial optimization problem that we term "bounded coordinated matching". While we show this problem is NP-hard, we give an efficient greedy solution, which inherits approximation bounds from supermodular minimization theory. Experiments on eight benchmark datasets show that the proposed approach is highly effective.