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Yishay Mansour
Researcher at Tel Aviv University
Publications - 546
Citations - 30407
Yishay Mansour is an academic researcher from Tel Aviv University. The author has contributed to research in topics: Regret & Upper and lower bounds. The author has an hindex of 80, co-authored 511 publications receiving 26984 citations. Previous affiliations of Yishay Mansour include Technion – Israel Institute of Technology & IBM.
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Proceedings Article
Online Learning with Low Rank Experts
TL;DR: In this article, the authors consider the problem of predicting with expert advice when the losses of the experts have low-dimensional structure: they are restricted to an unknown d-dimensional subspace, and devise algorithms with regret bounds that are independent of the number of experts and depend only on the rank d.
Journal ArticleDOI
Designing Committees for Mitigating Biases
TL;DR: A novel model of voting in which a committee of experts is constructed to reduce the biases of its members is studied, which is inherently different from the well-studied models of voting that focus on aggregation of preferences or on aggregating information due to the introduction of similarity biases.
Journal ArticleDOI
Combining Online Algorithms for Acceptance and Rejection
TL;DR: This work considers the problem of combining algorithms designed for each of these objectives in a way that is good under both measures simultaneously, and shows how to derive a combined algorithm with competitive ratio O(cRcA) for rejection and O(a) for acceptance.
Proceedings Article
Submultiplicative Glivenko-Cantelli and uniform convergence of revenues
TL;DR: In this paper, a variant of the classic Glivenko-cantelli Theorem, which asserts uniform convergence of the empirical Cumulative Distribution Function (CDF) to the CDF of the underlying distribution, is derived.
Posted Content
Multi-Armed Bandits with Metric Movement Costs
TL;DR: In this article, the authors consider the non-stochastic MAB problem with Lipschitz loss functions and derive a tight regret bound of Ω(T^{\frac{d+1}{d+2}) where d is the Minkowski dimension of the space.