24 citations · 48 across the 8 of their papers we have counts for
10 papers
Shape And Structure Preserving Differential Privacy
Carlos Soto, Karthik Bharath, Matthew Reimherr +1
It is common for data structures such as images and shapes of 2D objects to be represented as points on a manifold. The utility of a mechanism to produce sanitized differentially p…
Exact Privacy Guarantees for Markov Chain Implementations of the Exponential Mechanism with Artificial Atoms
Jeremy Seeman, Matthew Reimherr, Aleksandra Slavkovic
Implementations of the exponential mechanism in differential privacy often require sampling from intractable distributions. When approximate procedures like Markov chain Monte Carl…
Differential Privacy Over Riemannian Manifolds
Matthew Reimherr, Karthik Bharath, Carlos Soto
In this work we consider the problem of releasing a differentially private statistical summary that resides on a Riemannian manifold. We present an extension of the Laplace or K-no…
An Efficient Semi-smooth Newton Augmented Lagrangian Method for Elastic Net
Tobia Boschi, Matthew Reimherr, Francesca Chiaromonte
Feature selection is an important and active research area in statistics and machine learning. The Elastic Net is often used to perform selection when the features present non-negl…
Adaptive Function-on-Scalar Regression with a Smoothing Elastic Net
Ardalan Mirshani, Matthew Reimherr
This paper presents a new methodology, called AFSSEN, to simultaneously select significant predictors and produce smooth estimates in a high-dimensional function-on-scalar linear m…
Optimal Function-on-Scalar Regression over Complex Domains
Matthew Reimherr, Bharath Sriperumbudur, Hyun Bin Kang
In this work we consider the problem of estimating function-on-scalar regression models when the functions are observed over multi-dimensional or manifold domains and with potentia…