Adaptive Region-Based Active Learning
This work addresses active learning for machine learning practitioners by providing an adaptive region-based method that is incremental, building on existing region-based approaches.
The paper tackles the problem of active learning by introducing an algorithm that adaptively partitions the input space into regions and trains distinct predictors for each, with theoretical guarantees and experiments showing substantial empirical benefits over baselines, such as improved accuracy and reduced label complexity.
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.