Open Problem: Is There an Online Learning Algorithm That Learns Whenever Online Learning Is Possible?
Machine Learning
2021-07-21 v1 Artificial Intelligence
Probability
Statistics Theory
Machine Learning
Statistics Theory
Abstract
This open problem asks whether there exists an online learning algorithm for binary classification that guarantees, for all target concepts, to make a sublinear number of mistakes, under only the assumption that the (possibly random) sequence of points X allows that such a learning algorithm can exist for that sequence. As a secondary problem, it also asks whether a specific concise condition completely determines whether a given (possibly random) sequence of points X admits the existence of online learning algorithms guaranteeing a sublinear number of mistakes for all target concepts.
Cite
@article{arxiv.2107.09542,
title = {Open Problem: Is There an Online Learning Algorithm That Learns Whenever Online Learning Is Possible?},
author = {Steve Hanneke},
journal= {arXiv preprint arXiv:2107.09542},
year = {2021}
}