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08/11/2023
Private Distribution Learning with Public Data: The View from Sample Compression
We study the problem of private distribution learning with access to pub...
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03/07/2023
Exploring the Limits of Indiscriminate Data Poisoning Attacks
Indiscriminate data poisoning attacks aim to decrease a model's test acc...
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03/02/2023
Choosing Public Datasets for Private Machine Learning via Gradient Subspace Distance
Differentially private stochastic gradient descent privatizes model trai...
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02/06/2023
Private GANs, Revisited
We show that the canonical approach for training differentially private ...
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01/30/2023
A Bias-Variance-Privacy Trilemma for Statistical Estimation
The canonical algorithm for differentially private mean estimation is to...
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12/21/2022
Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks
We introduce camouflaged data poisoning attacks, a new attack vector tha...
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12/13/2022
Considerations for Differentially Private Learning with Large-Scale Public Pretraining
The performance of differentially private machine learning can be booste...
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12/09/2022
Robustness Implies Privacy in Statistical Estimation
We study the relationship between adversarial robustness and differentia...
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08/16/2022
Private Estimation with Public Data
We initiate the study of differentially private (DP) estimation with acc...
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06/06/2022
Per-Instance Privacy Accounting for Differentially Private Stochastic Gradient Descent
Differentially private stochastic gradient descent (DP-SGD) is the workh...
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05/17/2022
New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma
We prove new lower bounds for statistical estimation tasks under the con...
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04/19/2022
Indiscriminate Data Poisoning Attacks on Neural Networks
Data poisoning attacks, in which a malicious adversary aims to influence...
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01/27/2022
Calibration with Privacy in Peer Review
Reviewers in peer review are often miscalibrated: they may be strict, le...
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11/25/2021
Efficient Mean Estimation with Pure Differential Privacy via a Sum-of-Squares Exponential Mechanism
We give the first polynomial-time algorithm to estimate the mean of a d-...
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11/09/2021
The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection
Hyperparameter optimization is a ubiquitous challenge in machine learnin...
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11/08/2021
A Private and Computationally-Efficient Estimator for Unbounded Gaussians
We give the first polynomial-time, polynomial-sample, differentially pri...
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10/27/2021
Unbiased Statistical Estimation and Valid Confidence Intervals Under Differential Privacy
We present a method for producing unbiased parameter estimates and valid...
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10/13/2021
Differentially Private Fine-tuning of Language Models
We give simpler, sparser, and faster algorithms for differentially priva...
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06/02/2021
Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data
We study stochastic convex optimization with heavy-tailed data under the...
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03/04/2021
Remember What You Want to Forget: Algorithms for Machine Unlearning
We study the problem of forgetting datapoints from a learnt model. In th...
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10/19/2020
On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians
We provide sample complexity upper bounds for agnostically learning mult...
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10/18/2020
Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization
A common pain point in differentially private machine learning is the si...
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06/11/2020
CoinPress: Practical Private Mean and Covariance Estimation
We present simple differentially private estimators for the mean and cov...
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04/30/2020
A Primer on Private Statistics
Differentially private statistical estimation has seen a flurry of devel...
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03/31/2020
The Discrete Gaussian for Differential Privacy
We show how to efficiently provide differentially private answers to cou...
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02/27/2020
PAPRIKA: Private Online False Discovery Rate Control
In hypothesis testing, a false discovery occurs when a hypothesis is inc...
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02/21/2020
Locally Private Hypothesis Selection
We initiate the study of hypothesis selection under local differential p...
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02/21/2020
Private Mean Estimation of Heavy-Tailed Distributions
We give new upper and lower bounds on the minimax sample complexity of d...
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02/21/2020
Privately Learning Markov Random Fields
We consider the problem of learning Markov Random Fields (including the ...
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11/17/2019
Random Restrictions of High-Dimensional Distributions and Uniformity Testing with Subcube Conditioning
We give a nearly-optimal algorithm for testing uniformity of distributio...
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09/09/2019
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians
Learning the parameters of a Gaussian mixtures models is a fundamental a...
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05/30/2019
Private Hypothesis Selection
We provide a differentially private algorithm for hypothesis selection. ...
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05/28/2019
Private Identity Testing for High-Dimensional Distributions
In this work we present novel differentially private identity (goodness-...
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11/27/2018
The Structure of Optimal Private Tests for Simple Hypotheses
Hypothesis testing plays a central role in statistical inference, and is...
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07/17/2018
Anaconda: A Non-Adaptive Conditional Sampling Algorithm for Distribution Testing
We investigate distribution testing with access to non-adaptive conditio...
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05/01/2018
Privately Learning High-Dimensional Distributions
We design nearly optimal differentially private algorithms for learning ...
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03/07/2018
Sever: A Robust Meta-Algorithm for Stochastic Optimization
In high dimensions, most machine learning methods are brittle to even a ...
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02/28/2018
INSPECTRE: Privately Estimating the Unseen
We develop differentially private methods for estimating various distrib...
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02/20/2018
Actively Avoiding Nonsense in Generative Models
A generative model may generate utter nonsense when it is fit to maximiz...
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10/11/2017
Concentration of Multilinear Functions of the Ising Model with Applications to Network Data
We prove near-tight concentration of measure for polynomial functions of...
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04/12/2017
Robustly Learning a Gaussian: Getting Optimal Error, Efficiently
We study the fundamental problem of learning the parameters of a high-di...
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03/02/2017
Being Robust (in High Dimensions) Can Be Practical
Robust estimation is much more challenging in high dimensions than it is...
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04/21/2016
