
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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LargeScale Differentially Private BERT
In this work, we study the largescale pretraining of BERTLarge with di...
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Google COVID19 Vaccination Search Insights: Anonymization Process Description
This report describes the aggregation and anonymization process applied ...
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Private Counting from Anonymous Messages: NearOptimal Accuracy with Vanishing Communication Overhead
Differential privacy (DP) is a formal notion for quantifying the privacy...
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Locally Private kMeans in One Round
We provide an approximation algorithm for kmeans clustering in the one...
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On Deep Learning with Label Differential Privacy
In many machine learning applications, the training data can contain hig...
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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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Sampleefficient proper PAC learning with approximate differential privacy
In this paper we prove that the sample complexity of properly learning a...
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Robust and Private Learning of Halfspaces
In this work, we study the tradeoff between differential privacy and ad...
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ScaleFree Allocation, Amortized Convexity, and Myopic Weighted Paging
Inspired by Belady's optimal algorithm for unweighted paging, we conside...
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Online Linear Optimization with Many Hints
We study an online linear optimization (OLO) problem in which the learne...
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On Additive Approximate Submodularity
A realvalued set function is (additively) approximately submodular if i...
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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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Differentially Private Clustering: Tight Approximation Ratios
We study the task of differentially private clustering. For several basi...
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Neartight closure bounds for Littlestone and threshold dimensions
We study closure properties for the Littlestone and threshold dimensions...
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Fair Hierarchical Clustering
As machine learning has become more prevalent, researchers have begun to...
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A Note on Double Pooling Tests
We present double pooling, a simple, easytoimplement variation on test...
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Online Learning with Imperfect Hints
We consider a variant of the classical online linear optimization proble...
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Fair Correlation Clustering
In this paper, we study correlation clustering under fairness constraint...
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Pure Differentially Private Summation from Anonymous Messages
The shuffled (aka anonymous) model has recently generated significant in...
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Preventing Adversarial Use of Datasets through Fair CoreSet Construction
We propose improving the privacy properties of a dataset by publishing o...
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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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Testing Mixtures of Discrete Distributions
There has been significant study on the sample complexity of testing pro...
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Clustering without OverRepresentation
In this paper we consider clustering problems in which each point is end...
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On the Learnability of Deep Random Networks
In this paper we study the learnability of deep random networks from bot...
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SemiOnline Bipartite Matching
In this paper we introduce the semionline model that generalizes the cl...
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Fair Clustering Through Fairlets
We study the question of fair clustering under the disparate impact doc...
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Linear Additive Markov Processes
We introduce LAMP: the Linear Additive Markov Process. Transitions in LA...
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Conversational flow in Oxfordstyle debates
Public debates are a common platform for presenting and juxtaposing dive...
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Ravi Kumar
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