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09/27/2021
Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message
The shuffle model of differential privacy has attracted attention in the...
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08/03/2021
Large-Scale Differentially Private BERT
In this work, we study the large-scale pretraining of BERT-Large with di...
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07/02/2021
Google COVID-19 Vaccination Search Insights: Anonymization Process Description
This report describes the aggregation and anonymization process applied ...
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06/08/2021
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead
Differential privacy (DP) is a formal notion for quantifying the privacy...
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04/20/2021
Locally Private k-Means in One Round
We provide an approximation algorithm for k-means clustering in the one-...
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02/11/2021
On Deep Learning with Label Differential Privacy
In many machine learning applications, the training data can contain hig...
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12/16/2020
On Avoiding the Union Bound When Answering Multiple Differentially Private Queries
In this work, we study the problem of answering k queries with (ϵ, δ)-di...
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12/07/2020
Sample-efficient proper PAC learning with approximate differential privacy
In this paper we prove that the sample complexity of properly learning a...
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11/30/2020
Robust and Private Learning of Halfspaces
In this work, we study the trade-off between differential privacy and ad...
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11/18/2020
Scale-Free Allocation, Amortized Convexity, and Myopic Weighted Paging
Inspired by Belady's optimal algorithm for unweighted paging, we conside...
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10/06/2020
Online Linear Optimization with Many Hints
We study an online linear optimization (OLO) problem in which the learne...
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10/06/2020
On Additive Approximate Submodularity
A real-valued set function is (additively) approximately submodular if i...
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09/21/2020
On Distributed Differential Privacy and Counting Distinct Elements
We study the setup where each of n users holds an element from a discret...
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08/18/2020
Differentially Private Clustering: Tight Approximation Ratios
We study the task of differentially private clustering. For several basi...
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07/07/2020
Near-tight closure bounds for Littlestone and threshold dimensions
We study closure properties for the Littlestone and threshold dimensions...
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06/18/2020
Fair Hierarchical Clustering
As machine learning has become more prevalent, researchers have begun to...
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04/03/2020
A Note on Double Pooling Tests
We present double pooling, a simple, easy-to-implement variation on test...
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02/11/2020
Online Learning with Imperfect Hints
We consider a variant of the classical online linear optimization proble...
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02/06/2020
Fair Correlation Clustering
In this paper, we study correlation clustering under fairness constraint...
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02/05/2020
Pure Differentially Private Summation from Anonymous Messages
The shuffled (aka anonymous) model has recently generated significant in...
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10/24/2019
Preventing Adversarial Use of Datasets through Fair Core-Set Construction
We propose improving the privacy properties of a dataset by publishing o...
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08/29/2019
Private Heavy Hitters and Range Queries in the Shuffled Model
An exciting new development in differential privacy is the shuffled mode...
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07/06/2019
Testing Mixtures of Discrete Distributions
There has been significant study on the sample complexity of testing pro...
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05/29/2019
Clustering without Over-Representation
In this paper we consider clustering problems in which each point is end...
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04/08/2019
On the Learnability of Deep Random Networks
In this paper we study the learnability of deep random networks from bot...
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12/01/2018
Semi-Online Bipartite Matching
In this paper we introduce the semi-online model that generalizes the cl...
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02/15/2018
Fair Clustering Through Fairlets
We study the question of fair clustering under the disparate impact doc...
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04/05/2017
Linear Additive Markov Processes
We introduce LAMP: the Linear Additive Markov Process. Transitions in LA...
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04/11/2016
