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ORCIDMatthias Katzfuss
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2020 – today
- 2025
[i6]Felix Jimenez, Matthias Katzfuss:
Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks. CoRR abs/2501.04816 (2025)- 2023
- [j11]Myeongjong Kang
, Matthias Katzfuss:
Correlation-based sparse inverse Cholesky factorization for fast Gaussian-process inference. Stat. Comput. 33(3): 56 (2023) - [c2]Felix Jimenez, Matthias Katzfuss:
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes. AISTATS 2023: 1492-1512 - [c1]Jian Cao, Myeongjong Kang, Felix Jimenez, Huiyan Sang, Florian Tobias Schaefer, Matthias Katzfuss:
Variational Sparse Inverse Cholesky Approximation for Latent Gaussian Processes via Double Kullback-Leibler Minimization. ICML 2023: 3559-3576 - [i5]Jian Cao, Myeongjong Kang, Felix Jimenez, Huiyan Sang, Florian Schäfer, Matthias Katzfuss:
Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization. CoRR abs/2301.13303 (2023) - [i4]Felix Jimenez, Matthias Katzfuss:
Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks. CoRR abs/2305.17063 (2023) - [i3]Stephen Huan, Joseph Guinness, Matthias Katzfuss, Houman Owhadi, Florian Schäfer:
Sparse Cholesky factorization by greedy conditional selection. CoRR abs/2307.11648 (2023) - 2022
- [j10]Jian Cao, Joseph Guinness, Marc G. Genton, Matthias Katzfuss:
Scalable Gaussian-process regression and variable selection using Vecchia approximations. J. Mach. Learn. Res. 23: 348:1-348:30 (2022) - [j9]Matthias Katzfuss, Joseph Guinness, Earl Lawrence:
Scaled Vecchia Approximation for Fast Computer-Model Emulation. SIAM/ASA J. Uncertain. Quantification 10(2): 537-554 (2022) - [j8]Daniel Zilber, David R. Thompson, Matthias Katzfuss, Vijay Natraj, Jonathan Hobbs, Amy Braverman:
Spatial Surface Reflectance Retrievals for Visible/Shortwave Infrared Remote Sensing via Gaussian Process Priors. Remote. Sens. 14(9): 2183 (2022) - [j7]Marcin Jurek, Matthias Katzfuss:
Hierarchical sparse Cholesky decomposition with applications to high-dimensional spatio-temporal filtering. Stat. Comput. 32(1): 15 (2022) - [i2]Felix Jimenez, Matthias Katzfuss:
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes. CoRR abs/2203.01459 (2022) - 2021
- [j6]Daniel Zilber, Matthias Katzfuss:
Vecchia-Laplace approximations of generalized Gaussian processes for big non-Gaussian spatial data. Comput. Stat. Data Anal. 153: 107081 (2021) - [j5]Marcin Jurek, Matthias Katzfuss:
Multi-Resolution Filters for Massive Spatio-Temporal Data. J. Comput. Graph. Stat. 30(4): 1095-1110 (2021) - [j4]Jonathan Hobbs, Matthias Katzfuss, Daniel Zilber, Jenný Brynjarsdóttir, Anirban Mondal, Veronica J. Berrocal:
Spatial Retrievals of Atmospheric Carbon Dioxide from Satellite Observations. Remote. Sens. 13(4): 571 (2021) - [j3]Florian Schäfer, Matthias Katzfuss, Houman Owhadi:
Sparse Cholesky Factorization by Kullback-Leibler Minimization. SIAM J. Sci. Comput. 43(3): A2019-A2046 (2021) - 2020
- [i1]Florian Schäfer, Matthias Katzfuss, Houman Owhadi:
Sparse Cholesky factorization by Kullback-Leibler minimization. CoRR abs/2004.14455 (2020)
2010 – 2019
- 2017
- [j2]Matthias Katzfuss, Dorit Hammerling:
Parallel inference for massive distributed spatial data using low-rank models. Stat. Comput. 27(2): 363-375 (2017) - 2014
- [j1]Hai Nguyen, Matthias Katzfuss, Noel Cressie, Amy Braverman:
Spatio-Temporal Data Fusion for Very Large Remote Sensing Datasets. Technometrics 56(2): 174-185 (2014)
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