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20122026
most citedLimits of Private Learning with Access to Public Data

15 citations · 82 across the 26 of their papers we have counts for

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32 papers · 1 filter

cs.LG2026

Optimal Reconstruction from Linear Queries

Yuval Filmus, Shay Moran, Elizaveta Nesterova

We study the problem of reconstructing an unknown point in from approximate linear queries. This setting arises naturally in applications ranging from low-dimensiona…

cs.LG2026

Uniform Laws of Large Numbers in Product Spaces

Ron Holzman, Shay Moran, Alexander Shlimovich

Uniform laws of large numbers form a cornerstone of Vapnik--Chervonenkis theory, where they are characterized by the finiteness of the VC dimension. In this work, we study uniform…

cs.LG2026

Margin in Abstract Spaces

Yair Ashlagi, Roi Livni, Shay Moran +1

Margin-based learning, exemplified by linear and kernel methods, is one of the few classical settings where generalization guarantees are independent of the number of parameters. T…

cs.LG2023

Local Borsuk-Ulam, Stability, and Replicability

Zachary Chase, Bogdan Chornomaz, Shay Moran +1

We use and adapt the Borsuk-Ulam Theorem from topology to derive limitations on list-replicable and globally stable learning algorithms. We further demonstrate the applicability of…

cs.LG2023

Universal Rates for Multiclass Learning

Steve Hanneke, Shay Moran, Qian Zhang

We study universal rates for multiclass classification, establishing the optimal rates (up to log factors) for all hypothesis classes. This generalizes previous results on binary c…

cs.LG2023

Multiclass Boosting: Simple and Intuitive Weak Learning Criteria

Nataly Brukhim, Amit Daniely, Yishay Mansour +1

We study a generalization of boosting to the multiclass setting. We introduce a weak learning condition for multiclass classification that captures the original notion of weak lear…