Learning Active Learning from Data
Machine Learning
2017-07-17 v3
Abstract
In this paper, we suggest a novel data-driven approach to active learning (AL). The key idea is to train a regressor that predicts the expected error reduction for a candidate sample in a particular learning state. By formulating the query selection procedure as a regression problem we are not restricted to working with existing AL heuristics; instead, we learn strategies based on experience from previous AL outcomes. We show that a strategy can be learnt either from simple synthetic 2D datasets or from a subset of domain-specific data. Our method yields strategies that work well on real data from a wide range of domains.
Cite
@article{arxiv.1703.03365,
title = {Learning Active Learning from Data},
author = {Ksenia Konyushkova and Raphael Sznitman and Pascal Fua},
journal= {arXiv preprint arXiv:1703.03365},
year = {2017}
}