Accelerating Generalized Linear Models by Trading Off Computation for Uncertainty
Bayesian Generalized Linear Models (GLMs) define a flexible probabilistic framework to model categorical, ordinal and continuous data, …
Large-Scale Gaussian Processes via Alternating Projection
Gaussian process (GP) hyperparameter optimization requires repeatedly solving linear systems with n×n kernel matrices. To address the …
On the Disconnect Between Theory and Practice of Overparametrized Neural Networks
The infinite-width limit of neural networks (NNs) has garnered significant attention as a theoretical framework for analyzing the …
Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers
Linear partial differential equations (PDEs) are an important, widely applied class of mechanistic models, describing physical …
Posterior and Computational Uncertainty in Gaussian Processes
Gaussian processes scale prohibitively with the size of the dataset. In response, many approximation methods have been developed, which …
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization
Gaussian process hyperparameter optimization requires linear solves with, and log-determinants of, large kernel matrices. Iterative …
ProbNum: Probabilistic Numerics in Python
Probabilistic numerical methods (PNMs) solve numerical problems via probabilistic inference. They have been developed for linear …
Probabilistic Linear Solvers for Machine Learning
Linear systems are the bedrock of virtually all numerical computation. Machine learning poses specific challenges for the solution of …
Non-Parametric Calibration for Classification
Many applications of classification methods not only require high accuracy but also reliable estimation of predictive uncertainty. …
