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Proceedings of Machine Learning Research

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Proceedings of Machine Learning Research
PMLR · 2026-06-02 · via Proceedings of Machine Learning Research

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Volume 271: First International Conference on Probabilistic Numerics, 1-3 September 2025, EURECOM, Sophia Antipolis, France

[edit]

Editors: Motonobu Kanagawa, Jon Cockayne, Alexandra Gessner, Philipp Hennig

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Fixing the Pitfalls of Probabilistic Time-Series Forecasting Evaluation by Kernel Quadrature

; Proceedings of the First International Conference on Probabilistic Numerics, PMLR 271:1-11

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Adaptive Probabilistic ODE Solvers Without Adaptive Memory Requirements

Nicholas Krämer; Proceedings of the First International Conference on Probabilistic Numerics, PMLR 271:12-24

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Propagating Model Uncertainty through Filtering-based Probabilistic Numerical ODE Solvers

Dingling Yao, Filip Tronarp, Nathanael Bosch; Proceedings of the First International Conference on Probabilistic Numerics, PMLR 271:25-34

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Fast Gaussian process regression for high dimensional functions with derivative information

Aleksei Sorokin, Pieterjan Robbe, Fred J Hickernell; Proceedings of the First International Conference on Probabilistic Numerics, PMLR 271:35-49

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Natural Evolutionary Search meets Probabilistic Numerics

Pierre Osselin, Masaki Adachi, Xiaowen Dong, Michael A Osborne; Proceedings of the First International Conference on Probabilistic Numerics, PMLR 271:50-74

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Randomised Postiterations for Calibrated BayesCG

Niall Vyas, Disha Hegde, Jon Cockayne; Proceedings of the First International Conference on Probabilistic Numerics, PMLR 271:75-83

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A Dictionary of Closed-Form Kernel Mean Embeddings

Francois-Xavier Briol, Toni Karvonen, Alexandra Gessner, Maren Mahsereci; Proceedings of the First International Conference on Probabilistic Numerics, PMLR 271:84-94

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Effects of Interpolation Error and Bias on the Random Mesh Finite Element Method for Inverse Problems

Anne Poot, Iuri Rocha, Pierre Kerfriden, Frans van der Meer; Proceedings of the First International Conference on Probabilistic Numerics, PMLR 271:95-102

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Learning to Solve Related Linear Systems

Disha Hegde, Jon Cockayne; Proceedings of the First International Conference on Probabilistic Numerics, PMLR 271:103-121

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Bayesian autoregression to optimize temporal Matérn kernel Gaussian process hyperparameters

Wouter M. Kouw; Proceedings of the First International Conference on Probabilistic Numerics, PMLR 271:122-130

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Solving Einstein’s equations as Bayesian inference

Frederik De Ceuster, Tom Colemont, Tjonnie G.F. Li; Proceedings of the First International Conference on Probabilistic Numerics, PMLR 271:131-137

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Online Conformal Probabilistic Numerics via Adaptive Edge-Cloud Offloading

Qiushuo Hou, Sangwoo Park, Matteo Zecchin, Yunlong Cai, Guanding Yu, Osvaldo Simeone; Proceedings of the First International Conference on Probabilistic Numerics, PMLR 271:138-146

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