4 papers · 1 filter
Variational Deep Learning via Implicit Regularization
Jonathan Wenger, Beau Coker, Juraj Marusic +1
Modern deep learning models generalize remarkably well in-distribution, despite being overparametrized and trained with little to no explicit regularization. Instead, current theor…
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference
Jonathan Wenger, Kaiwen Wu, Philipp Hennig +3
Model selection in Gaussian processes scales prohibitively with the size of the training dataset, both in time and memory. While many approximations exist, all incur inevitable app…
Probabilistic Linear Solvers for Machine Learning
Jonathan Wenger, Philipp Hennig
Linear systems are the bedrock of virtually all numerical computation. Machine learning poses specific challenges for the solution of such systems due to their scale, characteristi…
Non-Parametric Calibration for Classification
Jonathan Wenger, Hedvig Kjellström, Rudolph Triebel
Many applications of classification methods not only require high accuracy but also reliable estimation of predictive uncertainty. However, while many current classification framew…