Raef Bassily | DeepAI

AI Chat

Featured Co-authors

Zhiwei Steven Wu
90 publications
Mikhail Belkin
57 publications
Shay Moran
56 publications
Mehryar Mohri
54 publications
Kunal Talwar
44 publications
Ananda Theertha Suresh
44 publications
Vitaly Feldman
41 publications
Raman Arora
36 publications
Noga Alon
35 publications
Jonathan Ullman
31 publications
Abhradeep Thakurta
26 publications

∙ 06/15/2023

Differentially Private Domain Adaptation with Theoretical Guarantees

In many applications, the labeled data at the learner's disposal is subj...

0 Raef Bassily, et al. ∙

∙ 02/24/2023

Differentially Private Algorithms for the Stochastic Saddle Point Problem with Optimal Rates for the Strong Gap

We show that convex-concave Lipschitz stochastic saddle point problems (...

0 Raef Bassily, et al. ∙

∙ 08/12/2022

Private Domain Adaptation from a Public Source

A key problem in a variety of applications is that of domain adaptation ...

1 Raef Bassily, et al. ∙

∙ 06/02/2022

Faster Rates of Convergence to Stationary Points in Differentially Private Optimization

We study the problem of approximating stationary points of Lipschitz and...

0 Raman Arora, et al. ∙

∙ 05/06/2022

Differentially Private Generalized Linear Models Revisited

We study the problem of (ϵ,δ)-differentially private learning of linear ...

0 Raman Arora, et al. ∙

∙ 04/21/2022

Differentially Private Learning with Margin Guarantees

We present a series of new differentially private (DP) algorithms with d...

0 Raef Bassily, et al. ∙

∙ 07/12/2021

Differentially Private Stochastic Optimization: New Results in Convex and Non-Convex Settings

We study differentially private stochastic optimization in convex and no...

0 Raef Bassily, et al. ∙

∙ 03/01/2021

Non-Euclidean Differentially Private Stochastic Convex Optimization

Differentially private (DP) stochastic convex optimization (SCO) is a fu...

0 Raef Bassily, et al. ∙

∙ 08/01/2020

Learning from Mixtures of Private and Public Populations

We initiate the study of a new model of supervised learning under privac...

0 Raef Bassily, et al. ∙

∙ 06/12/2020

Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses

Uniform stability is a notion of algorithmic stability that bounds the w...

0 Raef Bassily, et al. ∙

∙ 04/23/2020

Private Query Release Assisted by Public Data

We study the problem of differentially private query release assisted by...

0 Raef Bassily, et al. ∙

∙ 10/25/2019

Limits of Private Learning with Access to Public Data

We consider learning problems where the training set consists of two typ...

10 Noga Alon, et al. ∙

∙ 08/27/2019

Private Stochastic Convex Optimization with Optimal Rates

We study differentially private (DP) algorithms for stochastic convex op...

9 Raef Bassily, et al. ∙

∙ 07/31/2019

Privately Answering Classification Queries in the Agnostic PAC Model

We revisit the problem of differentially private release of classificati...

0 Raef Bassily, et al. ∙

∙ 11/06/2018

On exponential convergence of SGD in non-convex over-parametrized learning

Large over-parametrized models learned via stochastic gradient descent (...

6 Raef Bassily, et al. ∙

∙ 10/05/2018

Linear Queries Estimation with Local Differential Privacy

We study the problem of estimating a set of d linear queries with respec...

0 Raef Bassily, et al. ∙

∙ 03/14/2018

Model-Agnostic Private Learning via Stability

We design differentially private learning algorithms that are agnostic t...

0 Raef Bassily, et al. ∙

∙ 12/18/2017

The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning

Stochastic Gradient Descent (SGD) with small mini-batch is a key compone...

0 Siyuan Ma, et al. ∙

∙ 10/14/2017

Learners that Leak Little Information

We study learning algorithms that are restricted to revealing little inf...

0 Raef Bassily, et al. ∙

∙ 05/27/2014

Differentially Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds

In this paper, we initiate a systematic investigation of differentially ...

0 Raef Bassily, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world