Kalman filtering and smoothing are the foundational mechanisms for efficient inference in Gauss-Markov models. However, their time and …

Gaussian process (GP) hyperparameter optimization requires repeatedly solving linear systems with n×n kernel matrices. To address the …

Bayesian Generalized Linear Models (GLMs) define a flexible probabilistic framework to model categorical, ordinal and continuous data, …

The neural tangent kernel (NTK) has garnered significant attention as a theoretical framework for describing the behavior of …

Linear partial differential equations (PDEs) are an important, widely applied class of mechanistic models, describing physical …

Gaussian processes scale prohibitively with the size of the dataset. In response, many approximation methods have been developed, which …

Gaussian process hyperparameter optimization requires linear solves with, and log-determinants of, large kernel matrices. Iterative …

Probabilistic numerical methods (PNMs) solve numerical problems via probabilistic inference. They have been developed for linear …

Linear systems are the bedrock of virtually all numerical computation. Machine learning poses specific challenges for the solution of …

Many applications of classification methods not only require high accuracy but also reliable estimation of predictive uncertainty. …