4 citations · 5 across the 2 of their papers we have counts for
3 papers
Sample-efficient reinforcement learning using deep Gaussian processes
Charles Gadd, Markus Heinonen, Harri Lähdesmäki +1
Reinforcement learning provides a framework for learning to control which actions to take towards completing a task through trial-and-error. In many applications observing interact…
Enriched Mixtures of Gaussian Process Experts
Charles W. L. Gadd, Sara Wade, Alexis Boukouvalas
Mixtures of experts probabilistically divide the input space into regions, where the assumptions of each expert, or conditional model, need only hold locally. Combined with Gaussia…
Pseudo-marginal Bayesian inference for supervised Gaussian process latent variable models
Charles Gadd, Sara Wade, Akeel Shah +1
We introduce a Bayesian framework for inference with a supervised version of the Gaussian process latent variable model. The framework overcomes the high correlations between laten…