Bayesian Batch Active Learning as Sparse Subset Approximation

TL;DR

This paper introduces a Bayesian batch active learning method that efficiently selects diverse data points for labeling, improving scalability and performance in large-scale supervised learning tasks.

Contribution

It proposes a novel approach that approximates the complete data posterior to generate diverse batches, generalizes to arbitrary models with random projections, and demonstrates effectiveness on large-scale tasks.

Findings

Produces diverse batches for efficient active learning

Generalizes to arbitrary models using random projections

Shows improved performance on large-scale regression and classification tasks

Abstract

Leveraging the wealth of unlabeled data produced in recent years provides great potential for improving supervised models. When the cost of acquiring labels is high, probabilistic active learning methods can be used to greedily select the most informative data points to be labeled. However, for many large-scale problems standard greedy procedures become computationally infeasible and suffer from negligible model change. In this paper, we introduce a novel Bayesian batch active learning approach that mitigates these issues. Our approach is motivated by approximating the complete data posterior of the model parameters. While naive batch construction methods result in correlated queries, our algorithm produces diverse batches that enable efficient active learning at scale. We derive interpretable closed-form solutions akin to existing active learning procedures for linear models, and generalize to arbitrary models using random projections. We demonstrate the benefits of our approach on several large-scale regression and classification tasks.