Adaptive Active Learning for Regression via Reinforcement Learning

Simon D. Nguyen; Troy Russo; Kentaro Hoffman; Tyler H. McCormick

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arXiv · 2603.10435

Adaptive Active Learning for Regression via Reinforcement Learning

Simon D. Nguyen·Troy Russo·Kentaro Hoffman·Tyler H. McCormick

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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.

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Simon D. Nguyen, Troy Russo, Kentaro Hoffman, Tyler H. McCormick. 2026-03-11. Adaptive Active Learning for Regression via Reinforcement Learning. https://arxiv.org/abs/2603.10435

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