BayesSum: Bayesian Quadrature in Discrete Spaces by Sophia Seulkee Kang, Francois-Xavier Briol, Toni Karvonen, Zonghao Chen
Computation-Aware State-Space Models for Neural Data by Jonathan Raymond Huml, Jonathan Wenger, John Patrick Cunningham
Bayesian Inference of Discretization Error Means in ODEs via Ensemble Kalman Filtering by Shoji Toyota, Yuto Miyatake
Modelling Gas-Phase Reaction Kinetics with Guided Particle Diffusion Sampling by Andrew Millard, Zheng Zhao, Henrik Pedersen
Inter-Domain Gaussian Processes with Arbitrary Mean Functions in Factor Graphs by Alex Ledbetter, Hoang Minh Huu Nguyen, Lucas Carolus van Laake, Irene A. Kuling, Yoeri van de Burgt, Thijs van de Laar
Physics-Informed Machine Learning for Wind Turbulence Reconstruction with Lidar under Aircraft Motion by Amaury Capmas-Pernet, Christian Musso, Frédéric Dambreville, Tomline Michel
Calibrating Black-Box Probabilistic Numerical Methods by Chris J. Oates, Juntao Chen, Markus Michael Rau
Composing Non-Conjugate Factor Graphs with Closed-Form Variational Inference by Mykola Lukashchuk, Kyrylo Yemets, Wouter M. Kouw, Dmitry Bagaev, Ismail Senoz, Jeff Beck, Bert de Vries
Initial value problem uncertainty propagation in ODEs by Mathias Van Gompel, Tom Colemont, Tjonnie G.-F. Li, Johan Suykens, Frederik De Ceuster
Statistical Finite Elements for Modal Problems by Timothy J. Rogers, Brandon J. O’Connell, Max D. Champneys
Scaling up Probabilistic PDE Simulators with Structured Volumetric Information by Tim Weiland, Marvin Pförtner, Philipp Hennig
Three Costs of Amortizing Gaussian Process Inference with Neural Processes by Robin Young
Stable Smoothing and Hyperparameter Optimization for Temporal Gaussian Process Regression by Tom Colemont, Frederik De Ceuster, Brecht Evens, Tjonnie G.-F. Li
Probabilistic Numerics for Hamiltonian Dynamics by Frederik De Ceuster, Tom Colemont, Mathias Van Gompel, Tjonnie G.-F. Li
