A randomness test for functional panels. - Piotr Kokoszka; Matthew Reimherr; Nikolas Wölfing (J. Multivar. Anal., 2016)

Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA. - Jordan Awan; Ana Kenney; Matthew Reimherr; Aleksandra B. Slavkovic (ICML, 2019)

Formal Privacy for Functional Data with Gaussian Perturbations. - Ardalan Mirshani; Matthew Reimherr; Aleksandra B. Slavkovic (ICML, 2019)

Elliptical Perturbations for Differential Privacy. - Matthew Reimherr; Jordan Awan (NeurIPS, 2019)

KNG: The K-Norm Gradient Mechanism. - Matthew Reimherr; Jordan Awan (NeurIPS, 2019)

Functional data analysis for computational biology. - Marzia A. Cremona; Hongyan Xu; Kateryna D. Makova; Matthew Reimherr; Francesca Chiaromonte; Pedro Madrigal (Bioinform., 2019)

Private Posterior Inference Consistent with Public Information: A Case Study in Small Area Estimation from Synthetic Census Data. - Jeremy Seeman; Aleksandra B. Slavkovic; Matthew Reimherr (PSD, 2020)

An Efficient Semi-smooth Newton Augmented Lagrangian Method for Elastic Net. - Tobia Boschi; Matthew Reimherr; Francesca Chiaromonte (CoRR, 2020)

Modern Multiple Imputation with Functional Data. - Aniruddha Rajendra Rao; Matthew Reimherr (CoRR, 2020)

A Highly-Efficient Group Elastic Net Algorithm with an Application to Function-On-Scalar Regression. - Tobia Boschi; Matthew Reimherr; Francesca Chiaromonte (NeurIPS, 2021)

Differential Privacy Over Riemannian Manifolds. - Matthew Reimherr; Karthik Bharath; Carlos Soto 002 (NeurIPS, 2021)

Exact Privacy Guarantees for Markov Chain Implementations of the Exponential Mechanism with Artificial Atoms. - Jeremy Seeman; Matthew Reimherr; Aleksandra B. Slavkovic (NeurIPS, 2021)

Non-linear Functional Modeling using Neural Networks. - Aniruddha Rajendra Rao; Matthew Reimherr (CoRR, 2021)

Adaptive function-on-scalar regression with a smoothing elastic net. - Ardalan Mirshani; Matthew Reimherr (J. Multivar. Anal., 2021)

Shape And Structure Preserving Differential Privacy. - Carlos Soto 002; Karthik Bharath; Matthew Reimherr; Aleksandra B. Slavkovic (NeurIPS, 2022)

A Formal Privacy Framework for Partially Private Data. - Jeremy Seeman; Aleksandra B. Slavkovic; Matthew Reimherr (CoRR, 2022)

On Transfer Learning in Functional Linear Regression. - Haotian Lin 002; Matthew Reimherr (CoRR, 2022)

Differentially Private Functional Summaries via the Independent Component Laplace Process. - Haotian Lin 002; Matthew Reimherr (CoRR, 2023)

Nonlinear Functional Modeling Using Neural Networks. - Aniruddha Rajendra Rao; Matthew Reimherr (J. Comput. Graph. Stat., 2023)

Modern non-linear function-on-function regression. - Aniruddha Rajendra Rao; Matthew Reimherr (Stat. Comput., 2023)

Smoothness Adaptive Hypothesis Transfer Learning. - Haotian Lin 002; Matthew Reimherr (ICML, 2024)

On Hypothesis Transfer Learning of Functional Linear Models. - Haotian Lin 002; Matthew Reimherr (ICML, 2024)

Differentially Private Quantile Regression. - Tran Tran; Matthew Reimherr; Aleksandra B. Slavkovic (PSD, 2024)

M2AD: Multi-Sensor Multi-System Anomaly Detection through Global Scoring and Calibrated Thresholding. - Sarah Alnegheimish; Zelin He; Matthew Reimherr; Akash Chandrayan; Abhinav Pradhan; Luca D'Angelo (AISTATS, 2025)

Gaussian Differentially Private Human Faces Under a Face Radial Curve Representation. - Carlos J. Soto 002; Matthew Reimherr; Aleksandra B. Slavkovic; Mark Shriver (ICLR, 2025)

FAStEN: An Efficient Adaptive Method for Feature Selection and Estimation in High-Dimensional Functional Regressions. - Tobia Boschi; Lorenzo Testa; Francesca Chiaromonte; Matthew Reimherr (J. Comput. Graph. Stat., 2025)

Model-Robust and Adaptive-Optimal Transfer Learning for Tackling Concept Shifts in Nonparametric Regression. - Haotian Lin 002; Matthew Reimherr (CoRR, 2025)

Harnessing Vision-Language Models for Time Series Anomaly Detection. - Zelin He; Sarah Alnegheimish; Matthew Reimherr (CoRR, 2025)