Bayesian Probabilistic Numerical Methods

13 February 2017

Papers citing "Bayesian Probabilistic Numerical Methods"

37 / 37 papers shown

Title
Online Conformal Probabilistic Numerics via Adaptive Edge-Cloud Offloading Qiushuo Hou Sangwoo Park Matteo Zecchin Yunlong Cai G. Yu Osvaldo Simeone 70 0 0 18 Mar 2025
Computation-Aware Kalman Filtering and Smoothing Marvin Pfortner Jonathan Wenger Jon Cockayne Philipp Hennig 88 3 0 13 Mar 2025
Data-Efficient Kernel Methods for Learning Differential Equations and Their Solution Operators: Algorithms and Error Analysis Yasamin Jalalian Juan Felipe Osorio Ramirez Alexander W. Hsu Bamdad Hosseini H. Owhadi 48 0 0 02 Mar 2025
Calibrated Computation-Aware Gaussian Processes Disha Hegde Mohamed Adil Jon Cockayne 30 4 0 11 Oct 2024
Error Analysis of Kernel/GP Methods for Nonlinear and Parametric PDEs Pau Batlle Yifan Chen Bamdad Hosseini H. Owhadi Andrew M. Stuart 34 17 0 08 May 2023
Posterior and Computational Uncertainty in Gaussian Processes Jonathan Wenger Geoff Pleiss Marvin Pfortner Philipp Hennig John P. Cunningham 80 19 0 30 May 2022
Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spaces Mojmír Mutný Andreas Krause 35 11 0 26 May 2022
Fenrir: Physics-Enhanced Regression for Initial Value Problems Filip Tronarp Nathanael Bosch Philipp Hennig 44 13 0 02 Feb 2022
ProbNum: Probabilistic Numerics in Python Jonathan Wenger Nicholas Kramer Marvin Pfortner Jonathan Schmidt Nathanael Bosch ... A. Gessner Toni Karvonen F. Briol Maren Mahsereci Philipp Hennig 57 17 0 03 Dec 2021
Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations Nicholas Kramer Jonathan Schmidt Philipp Hennig 32 18 0 22 Oct 2021
Computational Graph Completion H. Owhadi 8 24 0 20 Oct 2021
Piecewise monotone estimation in one-parameter exponential family Takeru Matsuda Yuto Miyatake 30 2 0 30 Aug 2021
Bayesian Numerical Methods for Nonlinear Partial Differential Equations Junyang Wang Jon Cockayne O. Chkrebtii T. Sullivan Chris J. Oates 59 19 0 22 Apr 2021
The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning Roberto Bondesan Max Welling 42 7 0 08 Mar 2021
Small Sample Spaces for Gaussian Processes Toni Karvonen 22 13 0 04 Mar 2021
Bayesian Quadrature on Riemannian Data Manifolds Christian Frohlich A. Gessner Philipp Hennig Bernhard Schölkopf Georgios Arvanitidis 29 4 0 12 Feb 2021
Probabilistic Iterative Methods for Linear Systems Jon Cockayne Ilse C. F. Ipsen Chris J. Oates T. W. Reid 19 11 0 23 Dec 2020
Stable Implementation of Probabilistic ODE Solvers Nicholas Kramer Philipp Hennig 102 20 0 18 Dec 2020
Calibrated Adaptive Probabilistic ODE Solvers Nathanael Bosch Philipp Hennig Filip Tronarp 41 29 0 15 Dec 2020
Probabilistic Numeric Convolutional Neural Networks Marc Finzi Roberto Bondesan Max Welling BDL AI4TS 31 13 0 21 Oct 2020
Probabilistic Linear Solvers for Machine Learning Jonathan Wenger Philipp Hennig 36 17 0 19 Oct 2020
Bayesian ODE Solvers: The Maximum A Posteriori Estimate Filip Tronarp Simo Sarkka Philipp Hennig 43 42 0 01 Apr 2020
Data-Driven Forward Discretizations for Bayesian Inversion Daniele Bigoni Yuming Chen Nicolas García Trillos Youssef Marzouk D. Sanz-Alonso AI4CE 36 11 0 18 Mar 2020
Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions Toni Karvonen George Wynne Filip Tronarp Chris J. Oates Simo Särkkä 26 39 0 29 Jan 2020
A Role for Symmetry in the Bayesian Solution of Differential Equations Junyang Wang Jon Cockayne Chris J. Oates 27 7 0 24 Jun 2019
On the well-posedness of Bayesian inverse problems J. Latz 24 48 0 26 Feb 2019
Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data Yinhao Zhu N. Zabaras P. Koutsourelakis P. Perdikaris PINN AI4CE 46 854 0 18 Jan 2019
A Modern Retrospective on Probabilistic Numerics Chris J. Oates T. Sullivan AI4CE 24 64 0 14 Jan 2019
On the positivity and magnitudes of Bayesian quadrature weights Toni Karvonen Motonobu Kanagawa Simo Särkkä 16 12 0 20 Dec 2018
Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence Xiu Yang D. Barajas-Solano G. Tartakovsky A. Tartakovsky 15 77 0 24 Nov 2018
Adversarial Uncertainty Quantification in Physics-Informed Neural Networks Yibo Yang P. Perdikaris AI4CE PINN 32 355 0 09 Nov 2018
Probabilistic Solutions To Ordinary Differential Equations As Non-Linear Bayesian Filtering: A New Perspective Filip Tronarp Hans Kersting Simo Särkkä Philipp Hennig 33 63 0 08 Oct 2018
Probabilistic Linear Solvers: A Unifying View Simon Bartels Jon Cockayne Ilse C. F. Ipsen Philipp Hennig 17 24 0 08 Oct 2018
Robust and Scalable Models of Microbiome Dynamics T. Gibson Georg Gerber 75 35 0 11 May 2018
Neural network augmented inverse problems for PDEs Jens Berg K. Nystrom 22 41 0 27 Dec 2017
Student-t Process Quadratures for Filtering of Non-Linear Systems with Heavy-Tailed Noise Jakub Prüher Filip Tronarp Toni Karvonen Simo Särkkä O. Straka 20 19 0 15 Mar 2017
Probabilistic Integration: A Role in Statistical Computation? François‐Xavier Briol Chris J. Oates Mark Girolami Michael A. Osborne Dino Sejdinovic 21 50 0 03 Dec 2015