@inproceedings{f776eaf5e84d4531b465d81edae3b0b7,
title = "Adaptive region-based active learning",
abstract = "We present a new active learning algorithm that adaptively partitions the input space into a finite number of regions, and subsequently seeks a distinct predictor for each region. We prove theoretical guarantees for both the generalization error and the label complexity of our algorithm, and analyze the number of regions defined by the algorithm under some mild assumptions. We also report the results of an extensive suite of experiments on several real-world datasets demonstrating substantial empirical benefits over existing single-region and non-adaptive region-based active learning baselines.",
author = "Corinna Cortes and Giulia DeSalvo and Claudio Gentile and Mehryar Mohri and Ningshan Zhang",
note = "Funding Information: This work was partly supported by NSF CCF-1535987, NSF IIS-1618662, and a Google Research Award. Much of NZ{\textquoteright}s research was done during her Ph.D. studies at New York University. We thank anonymous reviewers for their helpful comments and suggestions. Publisher Copyright: {\textcopyright} 37th International Conference on Machine Learning, ICML 2020.; 37th International Conference on Machine Learning, ICML 2020 ; Conference date: 13-07-2020 Through 18-07-2020",
year = "2020",
language = "English (US)",
series = "37th International Conference on Machine Learning, ICML 2020",
publisher = "International Machine Learning Society (IMLS)",
pages = "2122--2131",
editor = "Hal Daume and Aarti Singh",
booktitle = "37th International Conference on Machine Learning, ICML 2020",
}