A key challenge in many modern data analysis tasks is that user data are...
Synthetic control is a causal inference tool used to estimate the treatm...
Differential privacy (DP) is a mathematical privacy notion increasingly
...
We study the problem of robust distribution estimation under the Wassers...
We study the problem faced by a data analyst or platform that wishes to
...
The sequential hypothesis testing problem is a class of statistical anal...
The Wasserstein distance, rooted in optimal transport (OT) theory, is a
...
Despite recent widespread deployment of differential privacy, relatively...
Accurately analyzing and modeling online browsing behavior play a key ro...
Normalizing flow models have risen as a popular solution to the problem ...
In this work we consider the problem of online submodular maximization u...
Ensuring the privacy of training data is a growing concern since many ma...
In precision medicine, machine learning techniques have been commonly
pr...
In hypothesis testing, a false discovery occurs when a hypothesis is
inc...
We consider a generalization of the third degree price discrimination pr...
Federated learning (FL) is a machine learning setting where many clients...
In this work we introduce the DP-auto-GAN framework for synthetic data
g...
The change-point detection problem seeks to identify distributional chan...
The change-point detection problem seeks to identify distributional chan...
In this paper we develop the first algorithms for online submodular
mini...
The sensitivity metric in differential privacy, which is informally defi...
We study the design of differentially private algorithms for adaptive
an...
We consider the design of private prediction markets, financial markets
...
We consider the problem of fitting a linear model to data held by indivi...