2 citations · 2 across the 4 of their papers we have counts for
4 papers
People Can Accurately Predict Behavior of Complex Algorithms That Are Available, Compact, and Aligned
Lindsay Popowski, Helena Vasconcelos, Ignacio Javier Fernandez +4
Users trust algorithms more when they can predict the algorithms' behavior. Simple algorithms trivially yield predictively accurate mental models, but modern AI algorithms have oft…
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…
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…
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…