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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...
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07/27/2023
Samplable Anonymous Aggregation for Private Federated Data Analysis
We revisit the problem of designing scalable protocols for private stati...
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07/21/2023
Differentially Private Heavy Hitter Detection using Federated Analytics
In this work, we study practical heuristics to improve the performance o...
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06/07/2023
Fast Optimal Locally Private Mean Estimation via Random Projections
We study the problem of locally private mean estimation of high-dimensio...
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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...
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12/24/2022
Concentration of the Langevin Algorithm's Stationary Distribution
A canonical algorithm for log-concave sampling is the Langevin Algorithm...
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11/18/2022
Private Federated Statistics in an Interactive Setting
Privately learning statistics of events on devices can enable improved u...
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10/24/2022
Private Online Prediction from Experts: Separations and Faster Rates
Online prediction from experts is a fundamental problem in machine learn...
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10/24/2022
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise
Recovering linear subspaces from data is a fundamental and important tas...
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10/16/2022
Resolving the Mixing Time of the Langevin Algorithm to its Stationary Distribution for Log-Concave Sampling
Sampling from a high-dimensional distribution is a fundamental task in s...
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08/09/2022
Stronger Privacy Amplification by Shuffling for Rényi and Approximate Differential Privacy
The shuffle model of differential privacy has gained significant interes...
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07/18/2022
FLAIR: Federated Learning Annotated Image Repository
Cross-device federated learning is an emerging machine learning (ML) par...
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05/27/2022
Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss
A central issue in machine learning is how to train models on sensitive ...
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05/05/2022
Optimal Algorithms for Mean Estimation under Local Differential Privacy
We study the problem of mean estimation of â„“_2-bounded vectors under the...
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03/01/2022
Private Frequency Estimation via Projective Geometry
In this work, we propose a new algorithm ProjectiveGeometryResponse (PGR...
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02/22/2022
Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation
Computing the noisy sum of real-valued vectors is an important primitive...
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06/25/2021
Private Adaptive Gradient Methods for Convex Optimization
We study adaptive methods for differentially private convex optimization...
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03/02/2021
Private Stochastic Convex Optimization: Optimal Rates in â„“_1 Geometry
Stochastic convex optimization over an â„“_1-bounded domain is ubiquitous ...
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02/24/2021
Lossless Compression of Efficient Private Local Randomizers
Locally Differentially Private (LDP) Reports are commonly used for colle...
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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...
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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...
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12/02/2020
On the Error Resistance of Hinge Loss Minimization
Commonly used classification algorithms in machine learning, such as sup...
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10/27/2020
Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC
Various differentially private algorithms instantiate the exponential me...
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10/26/2020
Stochastic Optimization with Laggard Data Pipelines
State-of-the-art optimization is steadily shifting towards massively par...
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06/12/2020
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses
Uniform stability is a notion of algorithmic stability that bounds the w...
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05/10/2020
Private Stochastic Convex Optimization: Optimal Rates in Linear Time
We study differentially private (DP) algorithms for stochastic convex op...
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02/08/2020
Exploring the Memorization-Generalization Continuum in Deep Learning
Human learners appreciate that some facts demand memorization whereas ot...
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11/05/2019
Computational Separations between Sampling and Optimization
Two commonly arising computational tasks in Bayesian learning are Optimi...
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08/28/2019
Rényi Differential Privacy of the Sampled Gaussian Mechanism
The Sampled Gaussian Mechanism (SGM)---a composition of subsampling and ...
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08/27/2019
Private Stochastic Convex Optimization with Optimal Rates
We study differentially private (DP) algorithms for stochastic convex op...
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04/23/2019
Semi-Cyclic Stochastic Gradient Descent
We consider convex SGD updates with a block-cyclic structure, i.e. where...
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02/22/2019
Better Algorithms for Stochastic Bandits with Adversarial Corruptions
We study the stochastic multi-armed bandits problem in the presence of a...
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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...
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11/19/2018
Private Selection from Private Candidates
Differentially Private algorithms often need to select the best amongst ...
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08/20/2018
Privacy Amplification by Iteration
Many commonly used learning algorithms work by iteratively updating an i...
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06/19/2018
Online Linear Quadratic Control
We study the problem of controlling linear time-invariant systems with k...
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04/30/2018
Adversarially Robust Generalization Requires More Data
Machine learning models are often susceptible to adversarial perturbatio...
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03/05/2018
Online learning over a finite action set with limited switching
This paper studies the value of switching actions in the Prediction From...
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02/24/2018
Scalable Private Learning with PATE
The rapid adoption of machine learning has increased concerns about the ...
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08/26/2017
On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches
The recent, remarkable growth of machine learning has led to intense int...
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10/18/2016
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
Some machine learning applications involve training data that is sensiti...
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07/01/2016
Deep Learning with Differential Privacy
Machine learning techniques based on neural networks are achieving remar...
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03/14/2016
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
TensorFlow is an interface for expressing machine learning algorithms, a...
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11/20/2014
