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Gaussian Process Conditional Density Estimation
Vincent Dutordoir, Hugh Salimbeni, Marc Deisenroth +1
Conditional Density Estimation (CDE) models deal with estimating conditional distributions. The conditions imposed on the distribution are the inputs of the model. CDE is a challen…
GPdoemd: a Python package for design of experiments for model discrimination
Simon Olofsson, Lukas Hebing, Sebastian Niedenführ +2
Model discrimination identifies a mathematical model that usefully explains and predicts a given system's behaviour. Researchers will often have several models, i.e. hypotheses, ab…
Orthogonally Decoupled Variational Gaussian Processes
Hugh Salimbeni, Ching-An Cheng, Byron Boots +1
Gaussian processes (GPs) provide a powerful non-parametric framework for reasoning over functions. Despite appealing theory, its superlinear computational and memory complexities h…
Maximizing acquisition functions for Bayesian optimization
James T. Wilson, Frank Hutter, Marc Peter Deisenroth
Bayesian optimization is a sample-efficient approach to global optimization that relies on theoretically motivated value heuristics (acquisition functions) to guide its search proc…
Meta Reinforcement Learning with Latent Variable Gaussian Processes
Steindór Sæmundsson, Katja Hofmann, Marc Peter Deisenroth
Learning from small data sets is critical in many practical applications where data collection is time consuming or expensive, e.g., robotics, animal experiments or drug design. Me…
Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches
Simon Olofsson, Marc Peter Deisenroth, Ruth Misener
Healthcare companies must submit pharmaceutical drugs or medical devices to regulatory bodies before marketing new technology. Regulatory bodies frequently require transparent and…