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ORCIDMatthew Reimherr
Person information
- affiliation: Pennsylvania State University, Department of Statistics, University Park, PA, USA
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2020 – today
- 2024
[c12]Haotian Lin, Matthew Reimherr:
Smoothness Adaptive Hypothesis Transfer Learning. ICML 2024- [c11]Haotian Lin, Matthew Reimherr:
On Hypothesis Transfer Learning of Functional Linear Models. ICML 2024 - [c10]Tran Tran, Matthew Reimherr, Aleksandra B. Slavkovic:
Differentially Private Quantile Regression. PSD 2024: 18-34 - [i14]Haotian Lin, Matthew Reimherr:
Smoothness Adaptive Hypothesis Transfer Learning. CoRR abs/2402.14966 (2024) - [i13]Carlos Soto, Matthew Reimherr, Aleksandra B. Slavkovic, Mark Shriver:
Gaussian Differentially Private Human Faces Under a Face Radial Curve Representation. CoRR abs/2409.08301 (2024) - 2023
- [j5]Aniruddha Rajendra Rao
, Matthew Reimherr:
Nonlinear Functional Modeling Using Neural Networks. J. Comput. Graph. Stat. 32(4): 1248-1257 (2023) - [j4]Aniruddha Rajendra Rao, Matthew Reimherr:
Modern non-linear function-on-function regression. Stat. Comput. 33(6): 130 (2023) - [i12]Haotian Lin, Matthew Reimherr:
Differentially Private Functional Summaries via the Independent Component Laplace Process. CoRR abs/2309.00125 (2023) - 2022
- [c9]Carlos Soto, Karthik Bharath, Matthew Reimherr, Aleksandra B. Slavkovic:
Shape And Structure Preserving Differential Privacy. NeurIPS 2022 - [i11]Jeremy Seeman, Aleksandra B. Slavkovic, Matthew Reimherr:
A Formal Privacy Framework for Partially Private Data. CoRR abs/2204.01102 (2022) - [i10]Jeremy Seeman, Matthew Reimherr, Aleksandra B. Slavkovic:
Exact Privacy Guarantees for Markov Chain Implementations of the Exponential Mechanism with Artificial Atoms. CoRR abs/2204.01132 (2022) - [i9]Haotian Lin, Matthew Reimherr:
On Transfer Learning in Functional Linear Regression. CoRR abs/2206.04277 (2022) - [i8]Carlos Soto, Karthik Bharath, Matthew Reimherr, Aleksandra B. Slavkovic:
Shape And Structure Preserving Differential Privacy. CoRR abs/2209.12667 (2022) - 2021
- [j3]Ardalan Mirshani, Matthew Reimherr:
Adaptive function-on-scalar regression with a smoothing elastic net. J. Multivar. Anal. 185: 104765 (2021) - [c8]Tobia Boschi, Matthew Reimherr, Francesca Chiaromonte:
A Highly-Efficient Group Elastic Net Algorithm with an Application to Function-On-Scalar Regression. NeurIPS 2021: 9264-9277 - [c7]Matthew Reimherr, Karthik Bharath, Carlos Soto:
Differential Privacy Over Riemannian Manifolds. NeurIPS 2021: 12292-12303 - [c6]Jeremy Seeman, Matthew Reimherr, Aleksandra B. Slavkovic:
Exact Privacy Guarantees for Markov Chain Implementations of the Exponential Mechanism with Artificial Atoms. NeurIPS 2021: 13125-13136 - [i7]Aniruddha Rajendra Rao, Matthew Reimherr:
Non-linear Functional Modeling using Neural Networks. CoRR abs/2104.09371 (2021) - [i6]Aniruddha Rajendra Rao, Matthew Reimherr:
Modern Non-Linear Function-on-Function Regression. CoRR abs/2107.14151 (2021) - 2020
- [c5]Jeremy Seeman, Aleksandra B. Slavkovic, Matthew Reimherr:
Private Posterior Inference Consistent with Public Information: A Case Study in Small Area Estimation from Synthetic Census Data. PSD 2020: 323-336 - [i5]Tobia Boschi, Matthew Reimherr, Francesca Chiaromonte:
An Efficient Semi-smooth Newton Augmented Lagrangian Method for Elastic Net. CoRR abs/2006.03970 (2020) - [i4]Aniruddha Rajendra Rao, Matthew Reimherr:
Modern Multiple Imputation with Functional Data. CoRR abs/2011.12509 (2020)
2010 – 2019
- 2019
- [j2]Marzia A. Cremona, Hongyan Xu, Kateryna D. Makova, Matthew Reimherr, Francesca Chiaromonte, Pedro Madrigal:
Functional data analysis for computational biology. Bioinform. 35(17): 3211-3213 (2019) - [c4]Jordan Awan, Ana Kenney, Matthew Reimherr, Aleksandra B. Slavkovic:
Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA. ICML 2019: 374-384 - [c3]Ardalan Mirshani, Matthew Reimherr, Aleksandra B. Slavkovic:
Formal Privacy for Functional Data with Gaussian Perturbations. ICML 2019: 4595-4604 - [c2]Matthew Reimherr, Jordan Awan:
Elliptical Perturbations for Differential Privacy. NeurIPS 2019: 10185-10196 - [c1]Matthew Reimherr, Jordan Awan:
KNG: The K-Norm Gradient Mechanism. NeurIPS 2019: 10208-10219 - [i3]Jordan Awan, Ana Kenney, Matthew Reimherr, Aleksandra B. Slavkovic:
Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA. CoRR abs/1901.10864 (2019) - [i2]Matthew Reimherr, Jordan Awan:
Elliptical Perturbations for Differential Privacy. CoRR abs/1905.09420 (2019) - [i1]Matthew Reimherr, Jordan Awan:
KNG: The K-Norm Gradient Mechanism. CoRR abs/1905.09436 (2019) - 2016
- [j1]Piotr Kokoszka, Matthew Reimherr, Nikolas Wölfing:
A randomness test for functional panels. J. Multivar. Anal. 151: 37-53 (2016)
Coauthor Index
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