# Nonparametric adaptive active learning under local smoothness condition

arXiv: https://arxiv.org/abs/2102.11077 · HTML: https://mlsanity.com/paper/2102.11077/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 22 Feb 2021.

## Builds on (most cited first, 3 of 3)

- [Rates of Convergence for Nearest Neighbor Classification](https://mlsanity.com/paper/1407.0067/) (2014)
- [Adaptivity to Noise Parameters in Nonparametric Active Learning](https://mlsanity.com/paper/1703.05841/) (2017)
- [K-NN active learning under local smoothness assumption](https://mlsanity.com/paper/2001.06485/) (2020)

## Built on by (most cited first, 2 of 2)

- [Neural Active Learning with Performance Guarantees](https://mlsanity.com/paper/2106.03243/) (2021)
- [A Two-Stage Active Learning Algorithm for k-Nearest Neighbors](https://mlsanity.com/paper/2211.10773/) (2022)

## Abstract (arXiv)

Active learning is typically used to label data, when the labeling process is expensive. Several active learning algorithms have been theoretically proved to perform better than their passive counterpart. However, these algorithms rely on some assumptions, which themselves contain some specific parameters. This paper adresses the problem of adaptive active learning in a nonparametric setting with minimal assumptions. We present a novel algorithm that is valid under more general assumptions than the previously known algorithms, and that can moreover adapt to the parameters used in these assumptions. This allows us to work with a larger class of distributions, thereby avoiding to exclude important densities like gaussians. Our algorithm achieves a minimax rate of convergence, and therefore performs almost as well as the best known non-adaptive algorithms.