Search Results for author: Noga Alon

Found 15 papers, 1 papers with code

A Theory of PAC Learnability of Partial Concept Classes

no code implementations18 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.

Dominance Solvability in Random Games

no code implementations22 May 2021 Noga Alon, Kirill Rudov, Leeat Yariv

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

Adversarial Laws of Large Numbers and Optimal Regret in Online Classification

no code implementations22 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.

General Classification online learning

Divisible subdivisions

no code implementations9 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

Closure Properties for Private Classification and Online Prediction

no code implementations10 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 $\cH$).

Classification General Classification +1

Boosting Simple Learners

1 code implementation31 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?


Limits of Private Learning with Access to Public Data

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.

Private PAC learning implies finite Littlestone dimension

no code implementations4 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.

A graph-theoretic approach to multitasking

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.

Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues

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.

Optimal compression of approximate inner products and dimension reduction

no code implementations2 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

Online Learning with Feedback Graphs: Beyond Bandits

no code implementations26 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.

online learning

Nonstochastic Multi-Armed Bandits with Graph-Structured Feedback

no code implementations30 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.

Multi-Armed Bandits online learning

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