Efficient and Parsimonious Agnostic Active Learning
Abstract
We develop a new active learning algorithm for the streaming settingsatisfying three important properties: 1) It provably works for anyclassifier representation and classification problem including thosewith severe noise. 2) It is efficiently implementable with an ERMoracle. 3) It is more aggressive than all previous approachessatisfying 1 and 2. To do this, we create an algorithm based on a newlydefined optimization problem and analyze it. We also conduct the firstexperimental analysis of all efficient agnostic active learningalgorithms, evaluating their strengths and weaknesses in differentsettings.
Cite
Text
Huang et al. "Efficient and Parsimonious Agnostic Active Learning." Neural Information Processing Systems, 2015.Markdown
[Huang et al. "Efficient and Parsimonious Agnostic Active Learning." Neural Information Processing Systems, 2015.](https://mlanthology.org/neurips/2015/huang2015neurips-efficient/)BibTeX
@inproceedings{huang2015neurips-efficient,
title = {{Efficient and Parsimonious Agnostic Active Learning}},
author = {Huang, Tzu-Kuo and Agarwal, Alekh and Hsu, Daniel J. and Langford, John and Schapire, Robert E.},
booktitle = {Neural Information Processing Systems},
year = {2015},
pages = {2755-2763},
url = {https://mlanthology.org/neurips/2015/huang2015neurips-efficient/}
}