Adaptive Region-Based Active Learning

• URL: http://arxiv.org/abs/2002.07348v1
• Date: Tue, 18 Feb 2020 03:16:36 GMT
• Title: Adaptive Region-Based Active Learning
• Authors: Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Ningshan Zhang
• Abstract summary: We present a new active learning algorithm that adaptively partitions the input space into a finite number of regions. We prove theoretical guarantees for both the generalization error and the label complexity of our algorithm. We report the results of an extensive suite of experiments on several real-world datasets.
• Score: 57.78835999208091
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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, both phases 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.

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