Batch Active Learning via Coordinated Matching
This work addresses the need for efficient batch labeling in scenarios like parallel label acquisition, offering a practical solution for reducing labeling effort in machine learning applications.
The paper tackles the problem of batch active learning for classifiers, where batches of examples are selected for labeling instead of single examples, by proposing a method that approximates sequential active-learning policies through Monte-Carlo simulation and combinatorial optimization, achieving high effectiveness on eight benchmark datasets.
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