Bayesian Probabilistic Numerical Methods

13 February 2017

Papers citing "Bayesian Probabilistic Numerical Methods"

26 / 26 papers shown

Title
Online Conformal Probabilistic Numerics via Adaptive Edge-Cloud Offloading Qiushuo Hou Sangwoo Park Matteo Zecchin Yunlong Cai G. Yu Osvaldo Simeone 62 0 0 18 Mar 2025
Computation-Aware Kalman Filtering and Smoothing Marvin Pfortner Jonathan Wenger Jon Cockayne Philipp Hennig 84 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 41 0 0 02 Mar 2025
Calibrated Computation-Aware Gaussian Processes Disha Hegde Mohamed Adil Jon Cockayne 14 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 24 17 0 08 May 2023
Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spaces Mojmír Mutný Andreas Krause 29 11 0 26 May 2022
Fenrir: Physics-Enhanced Regression for Initial Value Problems Filip Tronarp Nathanael Bosch Philipp Hennig 25 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 44 17 0 03 Dec 2021
Piecewise monotone estimation in one-parameter exponential family Takeru Matsuda Yuto Miyatake 17 2 0 30 Aug 2021
Bayesian Numerical Methods for Nonlinear Partial Differential Equations Junyang Wang Jon Cockayne O. Chkrebtii T. Sullivan Chris J. Oates 46 19 0 22 Apr 2021
The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning Roberto Bondesan Max Welling 34 7 0 08 Mar 2021
Small Sample Spaces for Gaussian Processes Toni Karvonen 11 13 0 04 Mar 2021
Bayesian Quadrature on Riemannian Data Manifolds Christian Frohlich A. Gessner Philipp Hennig Bernhard Schölkopf Georgios Arvanitidis 13 4 0 12 Feb 2021
Stable Implementation of Probabilistic ODE Solvers Nicholas Kramer Philipp Hennig 86 20 0 18 Dec 2020
Calibrated Adaptive Probabilistic ODE Solvers Nathanael Bosch Philipp Hennig Filip Tronarp 31 29 0 15 Dec 2020
Probabilistic Numeric Convolutional Neural Networks Marc Finzi Roberto Bondesan Max Welling BDL AI4TS 21 13 0 21 Oct 2020
Probabilistic Linear Solvers for Machine Learning Jonathan Wenger Philipp Hennig 20 17 0 19 Oct 2020
Bayesian ODE Solvers: The Maximum A Posteriori Estimate Filip Tronarp Simo Sarkka Philipp Hennig 33 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 17 11 0 18 Mar 2020
A Role for Symmetry in the Bayesian Solution of Differential Equations Junyang Wang Jon Cockayne Chris J. Oates 11 7 0 24 Jun 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 28 854 0 18 Jan 2019
Probabilistic Solutions To Ordinary Differential Equations As Non-Linear Bayesian Filtering: A New Perspective Filip Tronarp Hans Kersting Simo Särkkä Philipp Hennig 20 63 0 08 Oct 2018
Probabilistic Linear Solvers: A Unifying View Simon Bartels Jon Cockayne Ilse C. F. Ipsen Philipp Hennig 9 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 16 41 0 27 Dec 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