Matthew Reimherr

According to our database, Matthew Reimherr authored at least 24 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Gaussian Differentially Private Human Faces Under a Face Radial Curve Representation.

Carlos Soto, Matthew Reimherr, Aleksandra B. Slavkovic, Mark Shriver

CoRR, 2024

Differentially Private Quantile Regression.

Tran Tran, Matthew Reimherr, Aleksandra B. Slavkovic

Proceedings of the Privacy in Statistical Databases - International Conference, 2024

On Hypothesis Transfer Learning of Functional Linear Models.

Haotian Lin, Matthew Reimherr

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Smoothness Adaptive Hypothesis Transfer Learning.

Haotian Lin, Matthew Reimherr

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Modern non-linear function-on-function regression.

Aniruddha Rajendra Rao, Matthew Reimherr

Stat. Comput., December, 2023

Nonlinear Functional Modeling Using Neural Networks.

Aniruddha Rajendra Rao, Matthew Reimherr

J. Comput. Graph. Stat., 2023

Differentially Private Functional Summaries via the Independent Component Laplace Process.

Haotian Lin, Matthew Reimherr

CoRR, 2023

2022

On Transfer Learning in Functional Linear Regression.

Haotian Lin, Matthew Reimherr

CoRR, 2022

A Formal Privacy Framework for Partially Private Data.

Jeremy Seeman, Aleksandra B. Slavkovic, Matthew Reimherr

CoRR, 2022

Shape And Structure Preserving Differential Privacy.

Carlos Soto, Karthik Bharath, Matthew Reimherr, Aleksandra B. Slavkovic

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Adaptive function-on-scalar regression with a smoothing elastic net.

Ardalan Mirshani, Matthew Reimherr

J. Multivar. Anal., 2021

Non-linear Functional Modeling using Neural Networks.

Aniruddha Rajendra Rao, Matthew Reimherr

CoRR, 2021

Exact Privacy Guarantees for Markov Chain Implementations of the Exponential Mechanism with Artificial Atoms.

Jeremy Seeman, Matthew Reimherr, Aleksandra B. Slavkovic

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Differential Privacy Over Riemannian Manifolds.

Matthew Reimherr, Karthik Bharath, Carlos Soto

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A Highly-Efficient Group Elastic Net Algorithm with an Application to Function-On-Scalar Regression.

Tobia Boschi, Matthew Reimherr, Francesca Chiaromonte

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Modern Multiple Imputation with Functional Data.

Aniruddha Rajendra Rao, Matthew Reimherr

CoRR, 2020

An Efficient Semi-smooth Newton Augmented Lagrangian Method for Elastic Net.

Tobia Boschi, Matthew Reimherr, Francesca Chiaromonte

CoRR, 2020

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

Proceedings of the Privacy in Statistical Databases, 2020

2019

Functional data analysis for computational biology.

Marzia A. Cremona, Hongyan Xu, Kateryna D. Makova, Matthew Reimherr, Francesca Chiaromonte, Pedro Madrigal

Bioinform., 2019

KNG: The K-Norm Gradient Mechanism.

Matthew Reimherr, Jordan Awan

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Elliptical Perturbations for Differential Privacy.

Matthew Reimherr, Jordan Awan

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Formal Privacy for Functional Data with Gaussian Perturbations.

Ardalan Mirshani, Matthew Reimherr, Aleksandra B. Slavkovic

Proceedings of the 36th International Conference on Machine Learning, 2019

Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA.

Jordan Awan, Ana Kenney, Matthew Reimherr, Aleksandra B. Slavkovic

Proceedings of the 36th International Conference on Machine Learning, 2019

2016

A randomness test for functional panels.

Piotr Kokoszka, Matthew Reimherr, Nikolas Wölfing

J. Multivar. Anal., 2016