# Adaptive Region-Based Active Learning

arXiv: https://arxiv.org/abs/2002.07348 · HTML: https://mlsanity.com/paper/2002.07348/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2020.

## Built on by (most cited first, 10 of 10)

- [Meta Discovery: Learning to Discover Novel Classes given Very Limited Data](https://mlsanity.com/paper/2102.04002/) (2021)
- [Discrepancy-Based Active Learning for Domain Adaptation](https://mlsanity.com/paper/2103.03757/) (2021)
- [Budgeted Multiple-Expert Deferral](https://mlsanity.com/paper/2510.26706/) (2025)
- [Deep Active Ensemble Sampling For Image Classification](https://mlsanity.com/paper/2210.05770/) (2022)
- [Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigation](https://mlsanity.com/paper/2510.08962/) (2025)
- [Data-Efficient Learning via Minimizing Hyperspherical Energy](https://mlsanity.com/paper/2206.15204/) (2022)
- [Black-box Generalization of Machine Teaching](https://mlsanity.com/paper/2206.15205/) (2022)
- [A Survey of Learning on Small Data: Generalization, Optimization, and Challenge](https://mlsanity.com/paper/2207.14443/) (2022)
- [Generalization Error Bounds for Learning under Censored Feedback](https://mlsanity.com/paper/2404.09247/) (2024)
- [Cost-Aware Learning](https://mlsanity.com/paper/2604.28020/) (2026)

## Abstract (arXiv)

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.