no code implementations • 31 Oct 2023 • Lukas Tatzel, Jonathan Wenger, Frank Schneider, Philipp Hennig
Bayesian Generalized Linear Models (GLMs) define a flexible probabilistic framework to model categorical, ordinal and continuous data, and are widely used in practice.
1 code implementation • 26 Oct 2023 • Kaiwen Wu, Jonathan Wenger, Haydn Jones, Geoff Pleiss, Jacob R. Gardner
Training and inference in Gaussian processes (GPs) require solving linear systems with $n\times n$ kernel matrices.
no code implementations • 29 Sep 2023 • Jonathan Wenger, Felix Dangel, Agustinus Kristiadi
Our empirical results demonstrate that this is not the case in optimization, uncertainty quantification or continual learning.
1 code implementation • 23 Dec 2022 • Marvin Pförtner, Ingo Steinwart, Philipp Hennig, Jonathan Wenger
Crucially, this probabilistic viewpoint allows to (1) quantify the inherent discretization error; (2) propagate uncertainty about the model parameters to the solution; and (3) condition on noisy measurements.
1 code implementation • 30 May 2022 • Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig, John P. Cunningham
For any method in this class, we prove (i) convergence of its posterior mean in the associated RKHS, (ii) decomposability of its combined posterior covariance into mathematical and computational covariances, and (iii) that the combined variance is a tight worst-case bound for the squared error between the method's posterior mean and the latent function.
1 code implementation • 3 Dec 2021 • Jonathan Wenger, Nicholas Krämer, Marvin Pförtner, Jonathan Schmidt, Nathanael Bosch, Nina Effenberger, Johannes Zenn, Alexandra Gessner, Toni Karvonen, François-Xavier Briol, Maren Mahsereci, Philipp Hennig
Probabilistic numerical methods (PNMs) solve numerical problems via probabilistic inference.
no code implementations • 1 Jul 2021 • Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner
While preconditioning is well understood in the context of CG, we demonstrate that it can also accelerate convergence and reduce variance of the estimates for the log-determinant and its derivative.
1 code implementation • NeurIPS 2020 • Jonathan Wenger, Philipp Hennig
Linear systems are the bedrock of virtually all numerical computation.
1 code implementation • 12 Jun 2019 • Jonathan Wenger, Hedvig Kjellström, Rudolph Triebel
Many applications of classification methods not only require high accuracy but also reliable estimation of predictive uncertainty.