Adaptive Active Learning for Regression via Reinforcement Learning
Abstract
Active learning for regression reduces labeling costs by selecting the most informative samples. Improved Greedy Sampling is a prominent method that balances feature-space diversity and output-space uncertainty using a static, multiplicative rule. We propose Weighted improved Greedy Sampling (WiGS), which replaces this framework with a dynamic, additive criterion. We formulate weight selection as a reinforcement learning problem, enabling an agent to adapt the exploration-investigation balance throughout learning. Experiments on 18 benchmark datasets and a synthetic environment show WiGS outperforms iGS and other baseline methods in both accuracy and labeling efficiency, particularly in domains with irregular data density where the baseline's multiplicative rule ignores high-error samples in dense regions.
Cite
@article{arxiv.2603.10435,
title = {Adaptive Active Learning for Regression via Reinforcement Learning},
author = {Simon D. Nguyen and Troy Russo and Kentaro Hoffman and Tyler H. McCormick},
journal= {arXiv preprint arXiv:2603.10435},
year = {2026}
}
Comments
33 pages, 103 figures. Main paper (8 pages, 4 figures) plus appendix with proofs and supplemental experimental results. Submitted to UAI2026. Codebase available at https://github.com/thatswhatsimonsaid/WeightedGreedySampling