Roi Livni | DeepAI

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Noah Golowich
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∙ 05/24/2023

Can Copyright be Reduced to Privacy?

There is an increasing concern that generative AI models may produce out...

0 Niva Elkin-Koren, et al. ∙

∙ 02/09/2023

Information Theoretic Lower Bounds for Information Theoretic Upper Bounds

We examine the relationship between the mutual information between the o...

0 Roi Livni, et al. ∙

∙ 06/07/2022

Better Best of Both Worlds Bounds for Bandits with Switching Costs

We study best-of-both-worlds algorithms for bandits with switching cost,...

0 Idan Amir, et al. ∙

∙ 04/19/2022

Making Progress Based on False Discoveries

We consider the question of adaptive data analysis within the framework ...

0 Roi Livni, et al. ∙

∙ 02/27/2022

Benign Underfitting of Stochastic Gradient Descent

We study to what extent may stochastic gradient descent (SGD) be underst...

0 Tomer Koren, et al. ∙

∙ 02/27/2022

Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization

We consider linear prediction with a convex Lipschitz loss, or more gene...

0 Idan Amir, et al. ∙

∙ 06/29/2021

Never Go Full Batch (in Stochastic Convex Optimization)

We study the generalization performance of full-batch optimization algor...

0 Idan Amir, et al. ∙

∙ 06/25/2021

Littlestone Classes are Privately Online Learnable

We consider the problem of online classification under a privacy constra...

0 Noah Golowich, et al. ∙

∙ 02/02/2021

Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games

Which classes can be learned properly in the online model? – that is, by...

0 Steve Hanneke, et al. ∙

∙ 02/01/2021

SGD Generalizes Better Than GD (And Regularization Doesn't Help)

We give a new separation result between the generalization performance o...

0 Idan Amir, et al. ∙

∙ 06/24/2020

A Limitation of the PAC-Bayes Framework

PAC-Bayes is a useful framework for deriving generalization bounds which...

0 Roi Livni, et al. ∙

∙ 03/13/2020

Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study

The notion of implicit bias, or implicit regularization, has been sugges...

0 Assaf Dauber, et al. ∙

∙ 03/01/2020

An Equivalence Between Private Classification and Online Prediction

We prove that every concept class with finite Littlestone dimension can ...

0 Mark Bun, et al. ∙

∙ 02/24/2020

Prediction with Corrupted Expert Advice

We revisit the fundamental problem of prediction with expert advice, in ...

0 Idan Amir, et al. ∙

∙ 06/01/2019

Graph-based Discriminators: Sample Complexity and Expressiveness

A basic question in learning theory is to identify if two distributions ...

0 Roi Livni, et al. ∙

∙ 02/13/2019

On the Expressive Power of Kernel Methods and the Efficiency of Kernel Learning by Association Schemes

We study the expressive power of kernel methods and the algorithmic feas...

0 Pravesh K. Kothari, et al. ∙

∙ 02/09/2019

Passing Tests without Memorizing: Two Models for Fooling Discriminators

We introduce two mathematical frameworks for foolability in the context ...

0 Olivier Bousquet, et al. ∙

∙ 06/04/2018

Private PAC learning implies finite Littlestone dimension

We show that every approximately differentially private learning algorit...

0 Noga Alon, et al. ∙

∙ 11/16/2017

On Communication Complexity of Classification Problems

This work introduces a model of distributed learning in the spirit of Ya...

0 Daniel M. Kane, et al. ∙

∙ 10/05/2014

On the Computational Efficiency of Training Neural Networks

It is well-known that neural networks are computationally hard to train....

0 Roi Livni, et al. ∙

∙ 04/26/2013

An Algorithm for Training Polynomial Networks

We consider deep neural networks, in which the output of each node is a ...

0 Roi Livni, et al. ∙

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