Y
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.
Papers
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Journal ArticleDOI
Results on learnability and the Vapnik-Chervonenkis dimension
TL;DR: The notion of dynamic sampling is introduced, wherein the number of examples examined may increase with the complexity of the target concept, and this method is used to establish the learnability of various concept classes with an infinite Vapnik-Chervonenkis dimension.
Proceedings ArticleDOI
Harmonic buffer management policy for shared memory switches
TL;DR: The harmonic policy is proposed, a new scheduling policy based on a system of inequalities and thresholds that achieves high throughput and easily adapts to changing load conditions and its throughput competitive ratio is almost optimal.
Posted Content
Online Linear Quadratic Control
TL;DR: In this paper, the authors studied the problem of controlling linear time-invariant systems with known noisy dynamics and adversarially chosen quadratic losses, and presented the first efficient online learning algorithms in this setting that guarantee $O(sqrt{T})$ regret under mild assumptions, where T is the time horizon.
Journal ArticleDOI
Phantom: a simple and effective flow control scheme
TL;DR: Phantom, a simple constant space algorithm for rate-based flow control, converges fast to a fair rate allocation while generating a moderate queue length and can be gradually introduced into installed-based TCP networks.
Journal ArticleDOI
Randomness in Private Computations
Eyal Kushilevitz,Yishay Mansour +1 more
TL;DR: It is shown how n players can compute the exclusive-or (xor) of n boolean inputs t-privately, using only O(t2 log (n/t) random bits, which significantly improves over the known lower bounds.