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Deep Gaussian Processes with Importance-Weighted Variational Inference

Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc P. Deisenroth

High-Dimensional Bayesian Optimization Using Low-Dimensional Feature Spaces

Riccardo Moriconi, Marc P. Deisenroth, K. S. Sesh Kumar

Variational Integrator Networks

Steindór Sæmundsson, Katja Hofmann, Marc P. Deisenroth

Maximizing Acquisition Functions for Bayesian Optimization

Bayesian optimization is a sample-efficient approach to global optimization that relies on theoretically motivated value heuristics …

James T. Wilson, Frank Hutter, Marc P. Deisenroth

Orthogonally Decoupled Variational Gaussian Processes

Hugh Salimbeni, Ching-an Cheng, Byron Boots, Marc P. Deisenroth

Meta Reinforcement Learning with Latent Variable Gaussian Processes

Steindór Sæmundsson, Katja Hofmann, Marc P. Deisenroth

Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control

Sanket Kamthe, Marc P. Deisenroth

Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches

Simon Olofsson, Marc P. Deisenroth, Ruth Misener

Gaussian Process Conditional Density Estimation

Vincent Dutordoir, Hugh Salimbeni, Marc P. Deisenroth, James Hensman

Doubly Stochastic Variational Inference for Deep Gaussian Processes

Hugh Salimbeni, Marc P. Deisenroth