Active Learning: Problem Settings and Recent Developments

2020/12/08 by Hideitsu Hino, Hino, Hideitsu · 3 citations
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Optimization and Search Problems #Teaching and Learning Programming

paper · pdf · doi:10.48550/arxiv.2012.04225

openalex publication_date 2020/12/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• An analysis of approximations for maximizing submodular set functions—I
• Auto-Encoding Variational Bayes
• On a Measure of the Information Provided by an Experiment
• Gradient-based learning applied to document recognition
• Can Active Learning Experience Be Transferred?

Cited by

• An AI-driven workflow for the accelerated optimization of cell-free protein synthesis
• Cost-effective Reduced-Order Modeling via Bayesian Active Learning
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• Learning Algorithms for Active Learning
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