research
∙
06/15/2023
Differentially Private Domain Adaptation with Theoretical Guarantees
In many applications, the labeled data at the learner's disposal is subj...
share
research
∙
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 (...
research
∙
08/12/2022
Private Domain Adaptation from a Public Source
A key problem in a variety of applications is that of domain adaptation ...
research
∙
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...
research
∙
05/06/2022
Differentially Private Generalized Linear Models Revisited
We study the problem of (ϵ,δ)-differentially private learning of linear ...
research
∙
04/21/2022
Differentially Private Learning with Margin Guarantees
We present a series of new differentially private (DP) algorithms with d...
research
∙
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...
research
∙
03/01/2021
Non-Euclidean Differentially Private Stochastic Convex Optimization
Differentially private (DP) stochastic convex optimization (SCO) is a fu...
research
∙
08/01/2020
Learning from Mixtures of Private and Public Populations
We initiate the study of a new model of supervised learning under privac...
research
∙
06/12/2020
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses
Uniform stability is a notion of algorithmic stability that bounds the w...
research
∙
04/23/2020
Private Query Release Assisted by Public Data
We study the problem of differentially private query release assisted by...
research
∙
10/25/2019
Limits of Private Learning with Access to Public Data
We consider learning problems where the training set consists of two typ...
research
∙
08/27/2019
Private Stochastic Convex Optimization with Optimal Rates
We study differentially private (DP) algorithms for stochastic convex op...
research
∙
07/31/2019
Privately Answering Classification Queries in the Agnostic PAC Model
We revisit the problem of differentially private release of classificati...
research
∙
11/06/2018
On exponential convergence of SGD in non-convex over-parametrized learning
Large over-parametrized models learned via stochastic gradient descent (...
research
∙
10/05/2018
Linear Queries Estimation with Local Differential Privacy
We study the problem of estimating a set of d linear queries with respec...
research
∙
03/14/2018
Model-Agnostic Private Learning via Stability
We design differentially private learning algorithms that are agnostic t...
research
∙
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...
research
∙
10/14/2017
Learners that Leak Little Information
We study learning algorithms that are restricted to revealing little inf...
research
∙
05/27/2014