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cs.LG2024
BALI: Learning Neural Networks via Bayesian Layerwise Inference
Richard Kurle, Alexej Klushyn, Ralf Herbrich
We introduce a new method for learning Bayesian neural networks, treating them as a stack of multivariate Bayesian linear regression models. The main idea is to infer the layerwise…
cs.LG2022★ 2 cited
On the detrimental effect of invariances in the likelihood for variational inference
Richard Kurle, Ralf Herbrich, Tim Januschowski +2
Variational Bayesian posterior inference often requires simplifying approximations such as mean-field parametrisation to ensure tractability. However, prior work has associated the…
cs.LG2021
A PAC-Bayesian Analysis of Distance-Based Classifiers: Why Nearest-Neighbour works!
Thore Graepel, Ralf Herbrich
Abstract We present PAC-Bayesian bounds for the generalisation error of the K-nearest-neighbour classifier (K-NN). This is achieved by casting the K-NN classifier into a kernel spa…