Vitaly Feldman

research

∙ 07/28/2023

Mean Estimation with User-level Privacy under Data Heterogeneity

A key challenge in many modern data analysis tasks is that user data are...

0 Rachel Cummings, et al. ∙

research

∙ 07/27/2023

Samplable Anonymous Aggregation for Private Federated Data Analysis

We revisit the problem of designing scalable protocols for private stati...

0 Kunal Talwar, et al. ∙

research

∙ 07/21/2023

Differentially Private Heavy Hitter Detection using Federated Analytics

In this work, we study practical heuristics to improve the performance o...

0 Karan Chadha, et al. ∙

research

∙ 06/07/2023

Fast Optimal Locally Private Mean Estimation via Random Projections

We study the problem of locally private mean estimation of high-dimensio...

0 Hilal Asi, et al. ∙

research

∙ 02/27/2023

Near-Optimal Algorithms for Private Online Optimization in the Realizable Regime

We consider online learning problems in the realizable setting, where th...

0 Hilal Asi, et al. ∙

research

∙ 11/18/2022

Private Federated Statistics in an Interactive Setting

Privately learning statistics of events on devices can enable improved u...

0 Audra McMillan, et al. ∙

research

∙ 10/24/2022

Private Online Prediction from Experts: Separations and Faster Rates

Online prediction from experts is a fundamental problem in machine learn...

0 Hilal Asi, et al. ∙

research

∙ 10/24/2022

Subspace Recovery from Heterogeneous Data with Non-isotropic Noise

Recovering linear subspaces from data is a fundamental and important tas...

0 John Duchi, et al. ∙

research

∙ 09/29/2022

No Free Lunch in "Privacy for Free: How does Dataset Condensation Help Privacy"

New methods designed to preserve data privacy require careful scrutiny. ...

0 Nicholas Carlini, et al. ∙

research

∙ 08/09/2022

Stronger Privacy Amplification by Shuffling for Rényi and Approximate Differential Privacy

The shuffle model of differential privacy has gained significant interes...

0 Vitaly Feldman, et al. ∙

research

∙ 05/05/2022

Optimal Algorithms for Mean Estimation under Local Differential Privacy

We study the problem of mean estimation of ℓ_2-bounded vectors under the...

0 Hilal Asi, et al. ∙

research

∙ 03/01/2022

Private Frequency Estimation via Projective Geometry

In this work, we propose a new algorithm ProjectiveGeometryResponse (PGR...

0 Vitaly Feldman, et al. ∙

research

∙ 03/02/2021

Private Stochastic Convex Optimization: Optimal Rates in ℓ_1 Geometry

Stochastic convex optimization over an ℓ_1-bounded domain is ubiquitous ...

0 Hilal Asi, et al. ∙

research

∙ 02/24/2021

Lossless Compression of Efficient Private Local Randomizers

Locally Differentially Private (LDP) Reports are commonly used for colle...

0 Vitaly Feldman, et al. ∙

research

∙ 12/23/2020

Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling

Recent work of Erlingsson, Feldman, Mironov, Raghunathan, Talwar, and Th...

0 Vitaly Feldman, et al. ∙

research

∙ 12/11/2020

When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning?

Modern machine learning models are complex and frequently encode surpris...

18 Gavin Brown, et al. ∙

research

∙ 08/25/2020

Individual Privacy Accounting via a Renyi Filter

We consider a sequential setting in which a single dataset of individual...

1 Vitaly Feldman, et al. ∙

research

∙ 08/09/2020

What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation

Deep learning algorithms are well-known to have a propensity for fitting...

64 Vitaly Feldman, et al. ∙

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...

0 Raef Bassily, et al. ∙

research

∙ 05/10/2020

Private Stochastic Convex Optimization: Optimal Rates in Linear Time

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

38 Vitaly Feldman, et al. ∙

research

∙ 11/24/2019

PAC learning with stable and private predictions

We study binary classification algorithms for which the prediction on an...

10 Yuval Dagan, et al. ∙

research

∙ 11/11/2019

Interaction is necessary for distributed learning with privacy or communication constraints

Local differential privacy (LDP) is a model where users send privatized ...

10 Yuval Dagan, et al. ∙

research

∙ 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. ∙

research

∙ 06/12/2019

Does Learning Require Memorization? A Short Tale about a Long Tail

State-of-the-art results on image recognition tasks are achieved using o...

7 Vitaly Feldman, et al. ∙

research

∙ 05/24/2019

The advantages of multiple classes for reducing overfitting from test set reuse

Excessive reuse of holdout data can lead to overfitting. However, there ...

2 Vitaly Feldman, et al. ∙

research

∙ 02/27/2019

High probability generalization bounds for uniformly stable algorithms with nearly optimal rate

Algorithmic stability is a classical approach to understanding and analy...

2 Vitaly Feldman, et al. ∙

research

∙ 12/24/2018

Generalization Bounds for Uniformly Stable Algorithms

Uniform stability of a learning algorithm is a classical notion of algor...

6 Vitaly Feldman, et al. ∙

research

∙ 11/29/2018

Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity

Sensitive statistics are often collected across sets of users, with repe...

4 Úlfar Erlingsson, et al. ∙

research

∙ 09/24/2018

Learning without Interaction Requires Separation

One of the key resources in large-scale learning systems is the number o...

2 Amit Daniely, et al. ∙

research

∙ 08/20/2018

Privacy Amplification by Iteration

Many commonly used learning algorithms work by iteratively updating an i...

0 Vitaly Feldman, et al. ∙

research

∙ 03/27/2018

Privacy-preserving Prediction

Ensuring differential privacy of models learned from sensitive user data...

0 Cynthia Dwork, et al. ∙

research

∙ 03/12/2018

The Everlasting Database: Statistical Validity at a Fair Price

The problem of handling adaptivity in data analysis, intentional or not,...

0 Blake Woodworth, et al. ∙

research

∙ 12/19/2017

Calibrating Noise to Variance in Adaptive Data Analysis

Datasets are often used multiple times and each successive analysis may ...

0 Vitaly Feldman, et al. ∙

research

∙ 06/15/2017

Generalization for Adaptively-chosen Estimators via Stable Median

Datasets are often reused to perform multiple statistical analyses in an...

0 Vitaly Feldman, et al. ∙

research

∙ 11/20/2016

Dealing with Range Anxiety in Mean Estimation via Statistical Queries

We give algorithms for estimating the expectation of a given real-valued...

0 Vitaly Feldman, et al. ∙

research

∙ 08/15/2016

Generalization of ERM in Stochastic Convex Optimization: The Dimension Strikes Back

In stochastic convex optimization the goal is to minimize a convex funct...

0 Vitaly Feldman, et al. ∙

research

∙ 08/07/2016

A General Characterization of the Statistical Query Complexity

Statistical query (SQ) algorithms are algorithms that have access to an ...

0 Vitaly Feldman, et al. ∙

research

∙ 07/11/2013

Statistical Active Learning Algorithms for Noise Tolerance and Differential Privacy

We describe a framework for designing efficient active learning algorith...

0 Maria-Florina Balcan, et al. ∙

research

∙ 11/05/2012

Learning using Local Membership Queries

We introduce a new model of membership query (MQ) learning, where the le...

0 Pranjal Awasthi, et al. ∙

research

∙ 03/25/2011

Distribution-Independent Evolvability of Linear Threshold Functions

Valiant's (2007) model of evolvability models the evolutionary process o...

0 Vitaly Feldman, et al. ∙

research

∙ 12/03/2010

Agnostic Learning of Monomials by Halfspaces is Hard

We prove the following strong hardness result for learning: Given a dist...

0 Vitaly Feldman, et al. ∙

Vitaly Feldman

Featured Co-authors