Search Results for author:
Found 9 papers, 6 papers with code
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, and are widely used in practice.
Large-Scale Gaussian Processes via Alternating Projection
Training and inference in Gaussian processes (GPs) require solving linear systems with $n\times n$ kernel matrices.
On the Disconnect Between Theory and Practice of Overparametrized Neural Networks
Our empirical results demonstrate that this is not the case in optimization, uncertainty quantification or continual learning.
Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers
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.
Posterior and Computational Uncertainty in Gaussian Processes
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.
ProbNum: Probabilistic Numerics in Python
Probabilistic numerical methods (PNMs) solve numerical problems via probabilistic inference.
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization
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.
Probabilistic Linear Solvers for Machine Learning
Linear systems are the bedrock of virtually all numerical computation.
Non-Parametric Calibration for Classification
Many applications of classification methods not only require high accuracy but also reliable estimation of predictive uncertainty.
