ICML 2020poster16 citations
Adaptive Region-Based Active Learning
Corinna Cortes, Giulia Desalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang
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, while actively requesting labels. 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.
BibTeX
@InProceedings{pmlr-v119-cortes20a,
title = {Adaptive Region-Based Active Learning},
author = {Cortes, Corinna and Desalvo, Giulia and Gentile, Claudio and Mohri, Mehryar and Zhang, Ningshan},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
pages = {2144--2153},
year = {2020},
editor = {III, Hal Daumé and Singh, Aarti},
volume = {119},
series = {Proceedings of Machine Learning Research},
month = {13--18 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v119/cortes20a/cortes20a.pdf},
url = {https://proceedings.mlr.press/v119/cortes20a.html},
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, while actively requesting labels. 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.}
}