Batch Active Learning via Coordinated Matching

TL;DR

This paper introduces a novel batch active learning method that approximates sequential policies using Monte-Carlo simulation and coordinated matching, enabling efficient batch selection for labeling.

Contribution

It proposes a new algorithm for batch active learning that matches the distribution of sequential policies, with an efficient greedy solution and theoretical approximation guarantees.

Findings

Effective on eight benchmark datasets

Outperforms existing batch active learning methods

Provides a scalable approximation to sequential policies

Abstract

Most prior work on active learning of classifiers has focused on sequentially selecting one unlabeled example at a time to be labeled in order to reduce the overall labeling effort. In many scenarios, however, it is desirable to label an entire batch of examples at once, for example, when labels can be acquired in parallel. This motivates us to study batch active learning, which iteratively selects batches of $k>1$ examples to be labeled. We propose a novel batch active learning method that leverages the availability of high-quality and efficient sequential active-learning policies by attempting to approximate their behavior when applied for $k$ steps. Specifically, our algorithm first uses Monte-Carlo simulation to estimate the distribution of unlabeled examples selected by a sequential policy over $k$ step executions. The algorithm then attempts to select a set of $k$ 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 in general, we give an efficient greedy solution, which inherits approximation bounds from supermodular minimization theory. Our experimental results on eight benchmark datasets show that the proposed approach is highly effective