@inproceedings{12c44b6bd194440eb82414435092e86d,
title = "Online learning with low rank experts",
abstract = "We consider the problem of prediction with expert advice when the losses of the experts have low-dimensional structure: they are restricted to an unknown d-dimensional subspace. We devise algorithms with regret bounds that are independent of the number of experts and depend only on the rank d. For the stochastic model we show a tight bound of Θp√ dTq, and extend it to a setting of an approximate d subspace. For the adversarial model we show an upper bound of Opd√ Tq and a lower bound of Ωp√ dTq.",
author = "Elad Hazan and Tomer Koren and Roi Livni and Yishay Mansour",
note = "Publisher Copyright: {\textcopyright} 2016 E. Hazan, T. Koren, R. Livni & Y. Mansour.; 29th Conference on Learning Theory, COLT 2016 ; Conference date: 23-06-2016 Through 26-06-2016",
year = "2016",
month = jun,
day = "6",
language = "אנגלית",
series = "Proceedings of Machine Learning Research",
publisher = "PMLR",
pages = "1096--1114",
editor = "Vitaly Feldman and Alexander Rakhlin and Ohad Shamir",
booktitle = "29th Annual Conference on Learning Theory",
}