# MaxiMin Active Learning in Overparameterized Model Classes}

arXiv: https://arxiv.org/abs/1905.12782 · HTML: https://mlsanity.com/paper/1905.12782/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2019.

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

- [Neural Active Learning with Performance Guarantees](https://mlsanity.com/paper/2106.03243/) (2021)
- [Active Learning with Neural Networks: Insights from Nonparametric Statistics](https://mlsanity.com/paper/2210.08367/) (2022)
- [Active Self-Supervised Learning: A Few Low-Cost Relationships Are All You Need](https://mlsanity.com/paper/2303.15256/) (2023)
- [Model-Change Active Learning in Graph-Based Semi-Supervised Learning](https://mlsanity.com/paper/2110.07739/) (2021)
- [Graph-based Active Learning for Semi-supervised Classification of SAR Data](https://mlsanity.com/paper/2204.00005/) (2022)
- [Stopping Criterion for Active Learning Based on Error Stability](https://mlsanity.com/paper/2104.01836/) (2021)
- [Poisson Reweighted Laplacian Uncertainty Sampling for Graph-based Active Learning](https://mlsanity.com/paper/2210.15786/) (2022)
- [Out-of-Distribution Learning with Human Feedback](https://mlsanity.com/paper/2408.07772/) (2024)
- [Active Learning via Classifier Impact and Greedy Selection for Interactive Image Retrieval](https://mlsanity.com/paper/2412.02310/) (2024)
- [Active Learning of Deep Neural Networks via Gradient-Free Cutting Planes](https://mlsanity.com/paper/2410.02145/) (2024)

## Abstract (arXiv)

Generating labeled training datasets has become a major bottleneck in Machine Learning (ML) pipelines. Active ML aims to address this issue by designing learning algorithms that automatically and adaptively select the most informative examples for labeling so that human time is not wasted labeling irrelevant, redundant, or trivial examples. This paper proposes a new approach to active ML with nonparametric or overparameterized models such as kernel methods and neural networks. In the context of binary classification, the new approach is shown to possess a variety of desirable properties that allow active learning algorithms to automatically and efficiently identify decision boundaries and data clusters.