Rachel Cummings | DeepAI

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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...

0 Rachel Cummings, et al. ∙

∙ 03/24/2023

Differentially Private Synthetic Control

Synthetic control is a causal inference tool used to estimate the treatm...

0 Saeyoung Rho, et al. ∙

∙ 03/01/2023

What Are the Chances? Explaining the Epsilon Parameter in Differential Privacy

Differential privacy (DP) is a mathematical privacy notion increasingly ...

0 Priyanka Nanayakkara, et al. ∙

∙ 02/02/2023

Robust Estimation under the Wasserstein Distance

We study the problem of robust distribution estimation under the Wassers...

0 Sloan Nietert, et al. ∙

∙ 09/13/2022

Optimal Data Acquisition with Privacy-Aware Agents

We study the problem faced by a data analyst or platform that wishes to ...

0 Rachel Cummings, et al. ∙

∙ 04/10/2022

Private Sequential Hypothesis Testing for Statisticians: Privacy, Error Rates, and Sample Size

The sequential hypothesis testing problem is a class of statistical anal...

0 Wanrong Zhang, et al. ∙

∙ 11/02/2021

Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis

The Wasserstein distance, rooted in optimal transport (OT) theory, is a ...

0 Sloan Nietert, et al. ∙

∙ 10/13/2021

"I need a better description”: An Investigation Into User Expectations For Differential Privacy

Despite recent widespread deployment of differential privacy, relatively...

0 Rachel Cummings, et al. ∙

∙ 08/15/2021

How we browse: Measurement and analysis of digital behavior

Accurately analyzing and modeling online browsing behavior play a key ro...

0 Yuliia Lut, et al. ∙

∙ 03/25/2021

Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation

Normalizing flow models have risen as a popular solution to the problem ...

0 Chris Waites, et al. ∙

∙ 10/24/2020

Differentially Private Online Submodular Maximization

In this work we consider the problem of online submodular maximization u...

0 Sebastian Perez-Salazar, et al. ∙

∙ 09/08/2020

Attribute Privacy: Framework and Mechanisms

Ensuring the privacy of training data is a growing concern since many ma...

0 Wanrong Zhang, et al. ∙

∙ 03/20/2020

Locally Interpretable Predictions of Parkinson's Disease Progression

In precision medicine, machine learning techniques have been commonly pr...

0 Qiaomei Li, et al. ∙

∙ 02/27/2020

PAPRIKA: Private Online False Discovery Rate Control

In hypothesis testing, a false discovery occurs when a hypothesis is inc...

0 Wanrong Zhang, et al. ∙

∙ 12/12/2019

Algorithmic Price Discrimination

We consider a generalization of the third degree price discrimination pr...

0 Rachel Cummings, et al. ∙

∙ 12/10/2019

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients...

33 Peter Kairouz, et al. ∙

∙ 12/06/2019

Differentially Private Mixed-Type Data Generation For Unsupervised Learning

In this work we introduce the DP-auto-GAN framework for synthetic data g...

0 Uthaipon Tantipongpipat, et al. ∙

∙ 10/03/2019

Privately detecting changes in unknown distributions

The change-point detection problem seeks to identify distributional chan...

0 Rachel Cummings, et al. ∙

∙ 08/29/2018

Differentially Private Change-Point Detection

The change-point detection problem seeks to identify distributional chan...

0 Rachel Cummings, et al. ∙

∙ 07/06/2018

Differentially Private Online Submodular Optimization

In this paper we develop the first algorithms for online submodular mini...

0 Adrian Rivera Cardoso, et al. ∙

∙ 04/23/2018

Individual Sensitivity Preprocessing for Data Privacy

The sensitivity metric in differential privacy, which is informally defi...

0 Rachel Cummings, et al. ∙

∙ 03/16/2018

Differential Privacy for Growing Databases

We study the design of differentially private algorithms for adaptive an...

0 Rachel Cummings, et al. ∙

∙ 02/24/2016

The Possibilities and Limitations of Private Prediction Markets

We consider the design of private prediction markets, financial markets ...

0 Rachel Cummings, et al. ∙

∙ 06/10/2015

Truthful Linear Regression

We consider the problem of fitting a linear model to data held by indivi...

0 Rachel Cummings, et al. ∙

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