32 citations · 39 across the 8 of their papers we have counts for
8 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…
Learning to Predict Usage Options of Product Reviews with LLM-Generated Labels
Leo Kohlenberg, Leonard Horns, Frederic Sadrieh +7
Annotating large datasets can be challenging. However, crowd-sourcing is often expensive and can lack quality, especially for non-trivial tasks. We propose a method of using LLMs a…
Hieros: Hierarchical Imagination on Structured State Space Sequence World Models
Paul Mattes, Rainer Schlosser, Ralf Herbrich
One of the biggest challenges to modern deep reinforcement learning (DRL) algorithms is sample efficiency. Many approaches learn a world model in order to train an agent entirely i…
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…
CRISP: A Probabilistic Model for Individual-Level COVID-19 Infection Risk Estimation Based on Contact Data
Ralf Herbrich, Rajeev Rastogi, Roland Vollgraf
We present CRISP (COVID-19 Risk Score Prediction), a probabilistic graphical model for COVID-19 infection spread through a population based on the SEIR model where we assume access…
Kernel Topic Models
Philipp Hennig, David Stern, Ralf Herbrich +1
Latent Dirichlet Allocation models discrete data as a mixture of discrete distributions, using Dirichlet beliefs over the mixture weights. We study a variation of this concept, in…