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2405.08971
Cited By
Computation-Aware Kalman Filtering and Smoothing
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
13 March 2025
Marvin Pfortner
Jonathan Wenger
Jon Cockayne
Philipp Hennig
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Papers citing
"Computation-Aware Kalman Filtering and Smoothing"
22 / 22 papers shown
Bayesian Natural Gradient Fine-Tuning of CLIP Models via Kalman Filtering
Hossein Abdi
Mingfei Sun
Wei Pan
VLM
235
0
0
03 Nov 2025
Scalable Gaussian Processes with Latent Kronecker Structure
Jihao Andreas Lin
Sebastian Ament
Maximilian Balandat
David Eriksson
José Miguel Hernández-Lobato
E. Bakshy
181
4
0
07 Jun 2025
Randomised Postiterations for Calibrated BayesCG
Niall Vyas
Disha Hegde
Jon Cockayne
350
0
0
05 Apr 2025
Learning to Solve Related Linear Systems
Disha Hegde
Jon Cockayne
257
0
0
21 Mar 2025
Online Conformal Probabilistic Numerics via Adaptive Edge-Cloud Offloading
Qiushuo Hou
Sangwoo Park
Matteo Zecchin
Yunlong Cai
G. Yu
Osvaldo Simeone
680
0
0
18 Mar 2025
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference
Neural Information Processing Systems (NeurIPS), 2024
Jonathan Wenger
Kaiwen Wu
Philipp Hennig
Jacob R. Gardner
Geoff Pleiss
John P. Cunningham
334
9
0
01 Nov 2024
Calibrated Computation-Aware Gaussian Processes
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Disha Hegde
Mohamed Adil
Jon Cockayne
247
5
0
11 Oct 2024
The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions
Neural Information Processing Systems (NeurIPS), 2023
Jonathan Schmidt
Philipp Hennig
Jorg Nick
Filip Tronarp
289
14
0
13 Jun 2023
Posterior and Computational Uncertainty in Gaussian Processes
Neural Information Processing Systems (NeurIPS), 2022
Jonathan Wenger
Geoff Pleiss
Marvin Pfortner
Philipp Hennig
John P. Cunningham
554
21
0
30 May 2022
Spatio-Temporal Variational Gaussian Processes
Neural Information Processing Systems (NeurIPS), 2021
Oliver Hamelijnck
William J. Wilkinson
Niki A. Loppi
Arno Solin
Theodoros Damoulas
AI4TS
196
47
0
02 Nov 2021
Probabilistic Linear Solvers for Machine Learning
Neural Information Processing Systems (NeurIPS), 2020
Jonathan Wenger
Philipp Hennig
244
18
0
19 Oct 2020
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
Geoff Pleiss
M. Jankowiak
David Eriksson
Anil Damle
Jacob R. Gardner
217
45
0
19 Jun 2020
Efficiently Sampling Functions from Gaussian Process Posteriors
International Conference on Machine Learning (ICML), 2020
James T. Wilson
Viacheslav Borovitskiy
Alexander Terenin
P. Mostowsky
M. Deisenroth
322
178
0
21 Feb 2020
Temporal Parallelization of Bayesian Smoothers
IEEE Transactions on Automatic Control (IEEE TAC), 2019
Simo Särkkä
Á. F. García-Fernández
336
50
0
30 May 2019
Learning Latent Dynamics for Planning from Pixels
Danijar Hafner
Timothy Lillicrap
Ian S. Fischer
Ruben Villegas
David R Ha
Honglak Lee
James Davidson
BDL
727
1,651
0
12 Nov 2018
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences
Motonobu Kanagawa
Philipp Hennig
Dino Sejdinovic
Bharath K. Sriperumbudur
GP
BDL
272
387
0
06 Jul 2018
Efficient Spatio-Temporal Gaussian Regression via Kalman Filtering
M. Todescato
Andrea Carron
R. Carli
G. Pillonetto
Luca Schenato
178
29
0
03 May 2017
Bayesian Probabilistic Numerical Methods
SIAM Review (SIAM Rev.), 2017
Jon Cockayne
Chris J. Oates
T. Sullivan
Mark Girolami
317
175
0
13 Feb 2017
On dimension reduction in Gaussian filters
A. Solonen
Tiangang Cui
J. Hakkarainen
Youssef Marzouk
162
25
0
26 Aug 2015
Probabilistic Numerics and Uncertainty in Computations
Proceedings of the Royal Society A (Proc. R. Soc. A), 2015
Philipp Hennig
Michael A. Osborne
Mark Girolami
216
321
0
03 Jun 2015
Probabilistic Interpretation of Linear Solvers
SIAM Journal on Optimization (SIAM J. Optim.), 2014
Philipp Hennig
200
109
0
10 Feb 2014
Infinite-dimensional Bayesian filtering for detection of quasi-periodic phenomena in spatio-temporal data
Arno Solin
Simo Särkkä
203
24
0
11 Mar 2013
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