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no code implementations • 18 Jul 2021 • Noga Alon, Steve Hanneke, Ron Holzman, Shay Moran

In fact we exhibit easy-to-learn partial concept classes which provably cannot be captured by the traditional PAC theory.

no code implementations • 22 May 2021 • Noga Alon, Kirill Rudov, Leeat Yariv

We study the effectiveness of iterated elimination of strictly-dominated actions in random games.

no code implementations • 22 Jan 2021 • Noga Alon, Omri Ben-Eliezer, Yuval Dagan, Shay Moran, Moni Naor, Eylon Yogev

Laws of large numbers guarantee that given a large enough sample from some population, the measure of any fixed sub-population is well-estimated by its frequency in the sample.

no code implementations • 9 Dec 2020 • Noga Alon, Michel Krivelevich

We prove that for every graph $H$ of maximum degree at most $3$ and for every positive integer $q$ there is a finite $f=f(H, q)$ such that every $K_f$-minor contains a subdivision of $H$ in which every edge is replaced by a path whose length is divisible by $q$.

Combinatorics 05C53, 05C83, 05C38

no code implementations • 10 Mar 2020 • Noga Alon, Amos Beimel, Shay Moran, Uri Stemmer

Let~$\cH$ be a class of boolean functions and consider a {\it composed class} $\cH'$ that is derived from~$\cH$ using some arbitrary aggregation rule (for example, $\cH'$ may be the class of all 3-wise majority-votes of functions in $\cH$).

1 code implementation • 31 Jan 2020 • Noga Alon, Alon Gonen, Elad Hazan, Shay Moran

(ii) Expressivity: Which tasks can be learned by boosting weak hypotheses from a bounded VC class?

no code implementations • NeurIPS 2019 • Noga Alon, Raef Bassily, Shay Moran

We consider learning problems where the training set consists of two types of examples: private and public.

no code implementations • 4 Jun 2018 • Noga Alon, Roi Livni, Maryanthe Malliaris, Shay Moran

We show that every approximately differentially private learning algorithm (possibly improper) for a class $H$ with Littlestone dimension~$d$ requires $\Omega\bigl(\log^*(d)\bigr)$ examples.

no code implementations • NeurIPS 2017 • Noga Alon, Daniel Reichman, Igor Shinkar, Tal Wagner, Sebastian Musslick, Jonathan D. Cohen, Tom Griffiths, Biswadip Dey, Kayhan Ozcimder

A key feature of neural network architectures is their ability to support the simultaneous interaction among large numbers of units in the learning and processing of representations.

no code implementations • NeurIPS 2017 • Noga Alon, Moshe Babaioff, Yannai A. Gonczarowski, Yishay Mansour, Shay Moran, Amir Yehudayoff

In this work we derive 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.

no code implementations • 2 Oct 2016 • Noga Alon, Bo'az Klartag

On the positive side, we provide a randomized polynomial time algorithm for a bipartite variant of the Johnson-Lindenstrauss lemma in which scalar products are approximated up to an additive error of at most $\varepsilon$.

Metric Geometry Combinatorics

no code implementations • 26 Feb 2015 • Noga Alon, Nicolò Cesa-Bianchi, Ofer Dekel, Tomer Koren

We study a general class of online learning problems where the feedback is specified by a graph.

no code implementations • 30 Sep 2014 • Noga Alon, Nicolò Cesa-Bianchi, Claudio Gentile, Shie Mannor, Yishay Mansour, Ohad Shamir

This naturally models several situations where the losses of different actions are related, and knowing the loss of one action provides information on the loss of other actions.

no code implementations • NeurIPS 2013 • Noga Alon, Nicolò Cesa-Bianchi, Claudio Gentile, Yishay Mansour

We consider the partial observability model for multi-armed bandits, introduced by Mannor and Shamir.

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