Online learning with low rank experts

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publication29th Annual Conference on Learning Theory
EditorsVitaly Feldman, Alexander Rakhlin, Ohad Shamir
Number of pages19
StatePublished - 6 Jun 2016
Event29th Conference on Learning Theory, COLT 2016 - New York, United States
Duration: 23 Jun 201626 Jun 2016

Publication series

NameProceedings of Machine Learning Research
ISSN (Electronic)2640-3498


Conference29th Conference on Learning Theory, COLT 2016
Country/TerritoryUnited States
CityNew York


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