ICML 2019oral24 citations
Adversarial Online Learning with noise
Abstract
We present and study models of adversarial online learning where the feedback observed by the learner is noisy, and the feedback is either full information feedback or bandit feedback. Specifically, we consider binary losses xored with the noise, which is a Bernoulli random variable. We consider both a constant noise rate and a variable noise rate. Our main results are tight regret bounds for learning with noise in the adversarial online learning model.
BibTeX
@InProceedings{pmlr-v97-resler19a,
title = {Adversarial Online Learning with noise},
author = {Resler, Alon and Mansour, Yishay},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {5429--5437},
year = {2019},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
volume = {97},
series = {Proceedings of Machine Learning Research},
month = {09--15 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v97/resler19a/resler19a.pdf},
url = {https://proceedings.mlr.press/v97/resler19a.html},
abstract = {We present and study models of adversarial online learning where the feedback observed by the learner is noisy, and the feedback is either full information feedback or bandit feedback. Specifically, we consider binary losses xored with the noise, which is a Bernoulli random variable. We consider both a constant noise rate and a variable noise rate. Our main results are tight regret bounds for learning with noise in the adversarial online learning model.}
}