251 citations · 487 across the 35 of their papers we have counts for
40 papers
On the Benefit of Blocking for Online Experiments with Skewed Data
Abbas Zaidi, Matthew Reimherr, Rina Friedberg +2
This paper advocates for using blocked (stratified) assignment in online A/B tests to handle highly skewed population data. While blocking yields only modest precision gains (5-10%…
Temporal Functional Factor Analysis of Brain Connectivity
Kyle Stanley, Nicole Lazar, Matthew Reimherr
Many analyses of functional magnetic resonance imaging (fMRI) examine functional connectivity (FC), or the statistical dependencies among distant brain regions. These analyses are…
Efficient Difference-in-Differences Estimation when Outcomes are Missing at Random
Lorenzo Testa, Edward H. Kennedy, Matthew Reimherr
The Difference-in-Differences (DiD) method is a fundamental tool for causal inference, yet its application is often complicated by missing data. Although recent work has developed…
Fixed-Gaussian Spectral Algorithms: Minimax Optimal Rates for Misspecified Learning and Transfer
Haotian Lin, Matthew Reimherr
The principal objective of this work is twofold within nonparametric regression settings: (1) to establish the minimax optimal convergence rates for fixed-bandwidth Gaussian kernel…
Doubly-Robust Functional Average Treatment Effect Estimation
Lorenzo Testa, Tobia Boschi, Francesca Chiaromonte +2
Understanding causal relationships in the presence of complex, structured data remains a central challenge in modern statistics and science in general. While traditional causal inf…
Functional Factor Modeling of Brain Connectivity
Kyle Stanley, Nicole Lazar, Matthew Reimherr
Many fMRI analyses examine functional connectivity, or statistical dependencies among remote brain regions. Yet popular methods for studying whole-brain functional connectivity oft…
Gaussian Differentially Private Human Faces Under a Face Radial Curve Representation
Carlos Soto, Matthew Reimherr, Aleksandra Slavkovic +1
In this paper we consider the problem of releasing a Gaussian Differentially Private (GDP) 3D human face. The human face is a complex structure with many features and inherently ti…
Smoothness Adaptive Hypothesis Transfer Learning
Haotian Lin, Matthew Reimherr
Many existing two-phase kernel-based hypothesis transfer learning algorithms employ the same kernel regularization across phases and rely on the known smoothness of functions to ob…
Differentially Private Synthetic Heavy-tailed Data
Tran Tran, Matthew Reimherr, Aleksandra Slavković
The U.S. Census Longitudinal Business Database (LBD) product contains employment and payroll information of all U.S. establishments and firms dating back to 1976 and is an invaluab…
Pure Differential Privacy for Functional Summaries with a Laplace-like Process
Haotian Lin, Matthew Reimherr
Many existing mechanisms for achieving differential privacy (DP) on infinite-dimensional functional summaries typically involve embedding these functional summaries into finite-dim…
FAStEN: An Efficient Adaptive Method for Feature Selection and Estimation in High-Dimensional Functional Regressions
Tobia Boschi, Lorenzo Testa, Francesca Chiaromonte +1
Functional regression analysis is an established tool for many contemporary scientific applications. Regression problems involving large and complex data sets are ubiquitous, and f…
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…
On Hypothesis Transfer Learning of Functional Linear Models
Haotian Lin, Matthew Reimherr
We study the transfer learning (TL) for the functional linear regression (FLR) under the Reproducing Kernel Hilbert Space (RKHS) framework, observing that the TL techniques in exis…
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…
Formal Privacy for Partially Private Data
Jeremy Seeman, Matthew Reimherr, Aleksandra Slavkovic
Differential privacy (DP) quantifies privacy loss by analyzing noise injected into output statistics. For non-trivial statistics, this noise is necessary to ensure finite privacy l…
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…
Modern Non-Linear Function-on-Function Regression
Aniruddha Rajendra Rao, Matthew Reimherr
We introduce a new class of non-linear function-on-function regression models for functional data using neural networks. We propose a framework using a hidden layer consisting of c…
Non-linear Functional Modeling using Neural Networks
Aniruddha Rajendra Rao, Matthew Reimherr
We introduce a new class of non-linear models for functional data based on neural networks. Deep learning has been very successful in non-linear modeling, but there has been little…
Modern Multiple Imputation with Functional Data
Aniruddha Rajendra Rao, Matthew Reimherr
This work considers the problem of fitting functional models with sparsely and irregularly sampled functional data. It overcomes the limitations of the state-of-the-art methods, wh…
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…
Fast and Fair Simultaneous Confidence Bands for Functional Parameters
Dominik Liebl, Matthew Reimherr
Quantifying uncertainty using confidence regions is a central goal of statistical inference. Despite this, methodologies for confidence bands in Functional Data Analysis are still…
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…
KNG: The K-Norm Gradient Mechanism
Matthew Reimherr, Jordan Awan
This paper presents a new mechanism for producing sanitized statistical summaries that achieve \emph{differential privacy}, called the \emph{K-Norm Gradient} Mechanism, or KNG. Thi…
Elliptical Perturbations for Differential Privacy
Matthew Reimherr, Jordan Awan
We study elliptical distributions in locally convex vector spaces, and determine conditions when they can or cannot be used to satisfy differential privacy (DP). A requisite condit…