Active Learning: Problem Settings and Recent Developments

TL;DR

This paper reviews active learning, a cost-effective approach for improving predictive models by adaptively selecting data samples for labeling, highlighting recent research, theoretical advances, and practical applications.

Contribution

It provides a comprehensive overview of active learning problem settings, recent developments, and application examples, emphasizing acquisition functions, theoretical analysis, and stopping criteria.

Findings

Overview of active learning problem settings

Recent research trends and advances

Application examples in material development and measurement

Abstract

In supervised learning, acquiring labeled training data for a predictive model can be very costly, but acquiring a large amount of unlabeled data is often quite easy. Active learning is a method of obtaining predictive models with high precision at a limited cost through the adaptive selection of samples for labeling. This paper explains the basic problem settings of active learning and recent research trends. In particular, research on learning acquisition functions to select samples from the data for labeling, theoretical work on active learning algorithms, and stopping criteria for sequential data acquisition are highlighted. Application examples for material development and measurement are introduced.