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1912.03549
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lgpr: An interpretable nonparametric method for inferring covariate effects from longitudinal data
7 December 2019
Juho Timonen
Henrik Mannerstrom
Aki Vehtari
Harri Lähdesmäki
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Papers citing
"lgpr: An interpretable nonparametric method for inferring covariate effects from longitudinal data"
4 / 4 papers shown
Computationally efficient multi-level Gaussian process regression for functional data observed under completely or partially regular sampling designs
Adam Gorm Hoffmann
C. T. Ekstrøm
Andreas Kryger Jensen
263
1
0
19 Jun 2024
Improving Neural Additive Models with Bayesian Principles
International Conference on Machine Learning (ICML), 2023
Kouroche Bouchiat
Alexander Immer
Hugo Yèche
Gunnar Rätsch
Vincent Fortuin
BDL
MedIm
690
14
0
26 May 2023
Additive Gaussian Processes Revisited
International Conference on Machine Learning (ICML), 2022
Xiaoyu Lu
A. Boukouvalas
J. Hensman
171
31
0
20 Jun 2022
Hierarchical Gaussian Processes with Wasserstein-2 Kernels
S. Popescu
D. Sharp
James H. Cole
Ben Glocker
347
5
0
28 Oct 2020
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