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06/09/2022
Strong Memory Lower Bounds for Learning Natural Models
We give lower bounds on the amount of memory required by one-pass stream...
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12/01/2021
The Price of Differential Privacy under Continual Observation
We study the accuracy of differentially private mechanisms in the contin...
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06/24/2021
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation
We present two sample-efficient differentially private mean estimators f...
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06/18/2021
Non-parametric Differentially Private Confidence Intervals for the Median
Differential privacy is a restriction on data processing algorithms that...
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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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11/11/2020
Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy
In this paper, we study the Empirical Risk Minimization (ERM) problem in...
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07/10/2020
Differentially Private Simple Linear Regression
Economics and social science research often require analyzing datasets o...
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09/20/2019
Manipulation Attacks in Local Differential Privacy
Local differential privacy is a widely studied restriction on distribute...
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06/21/2019
Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis
We design a general framework for answering adaptive statistical queries...
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12/17/2018
Differentially Private Empirical Risk Minimization in Non-interactive Local Model via Polynomial of Inner Product Approximation
In this paper, we study the Empirical Risk Minimization problem in the n...
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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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10/30/2018
Private Algorithms Can Always Be Extended
We consider the following fundamental question on ϵ-differential privacy...
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10/30/2018
Private Algorithms Can be Always Extended
We consider the following fundamental question on ϵ-differential privacy...
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10/04/2018
Revealing Network Structure, Confidentially: Improved Rates for Node-Private Graphon Estimation
Motivated by growing concerns over ensuring privacy on social networks, ...
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10/03/2018
From Soft Classifiers to Hard Decisions: How fair can we be?
A popular methodology for building binary decision-making classifiers in...
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08/04/2018
Distributed Differential Privacy via Mixnets
We consider the problem of designing scalable, robust protocols for comp...
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06/15/2018
The Limits of Post-Selection Generalization
While statistics and machine learning offers numerous methods for ensuri...
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05/02/2018
Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network
Generative Adversarial Networks (GANs) are a machine learning approach c...
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06/02/2017
Information, Privacy and Stability in Adaptive Data Analysis
Traditional statistical theory assumes that the analysis to be performed...
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05/27/2014
