# Adaptivity to Noise Parameters in Nonparametric Active Learning

arXiv: https://arxiv.org/abs/1703.05841 · HTML: https://mlsanity.com/paper/1703.05841/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2017.

## Built on by (most cited first, 12 of 12)

- [Exponential Savings in Agnostic Active Learning through Abstention](https://mlsanity.com/paper/2102.00451/) (2021)
- [Neural Active Learning with Performance Guarantees](https://mlsanity.com/paper/2106.03243/) (2021)
- [Efficient Active Learning with Abstention](https://mlsanity.com/paper/2204.00043/) (2022)
- [Active Learning with Neural Networks: Insights from Nonparametric Statistics](https://mlsanity.com/paper/2210.08367/) (2022)
- [Improved Algorithms for Neural Active Learning](https://mlsanity.com/paper/2210.00423/) (2022)
- [Neural Active Learning Beyond Bandits](https://mlsanity.com/paper/2404.12522/) (2024)
- [Nuances in Margin Conditions Determine Gains in Active Learning](https://mlsanity.com/paper/2110.08418/) (2021)
- [A Weighted K-Center Algorithm for Data Subset Selection](https://mlsanity.com/paper/2312.10602/) (2023)
- [Nonparametric adaptive active learning under local smoothness condition](https://mlsanity.com/paper/2102.11077/) (2021)
- [A Two-Stage Active Learning Algorithm for k-Nearest Neighbors](https://mlsanity.com/paper/2211.10773/) (2022)
- [Nonparametric active learning for cost-sensitive classification](https://mlsanity.com/paper/2310.00511/) (2023)
- [Active Learning via Regression Beyond Realizability](https://mlsanity.com/paper/2506.00316/) (2025)

## Abstract (arXiv)

This work addresses various open questions in the theory of active learning for nonparametric classification. Our contributions are both statistical and algorithmic: -We establish new minimax-rates for active learning under common textitnoise conditions. These rates display interesting transitions -- due to the interaction between noise textitsmoothness and margin -- not present in the passive setting. Some such transitions were previously conjectured, but remained unconfirmed. -We present a generic algorithmic strategy for adaptivity to unknown noise smoothness and margin; our strategy achieves optimal rates in many general situations; furthermore, unlike in previous work, we avoid the need for textitadaptive confidence sets, resulting in strictly milder distributional requirements.