667 citations · 1.5k across the 26 of their papers we have counts for
4 papers · 1 filter
Deep Structured Mixtures of Gaussian Processes
Martin Trapp, Robert Peharz, Franz Pernkopf +1
Gaussian Processes (GPs) are powerful non-parametric Bayesian regression models that allow exact posterior inference, but exhibit high computational and memory costs. In order to i…
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos
Paavo Parmas, Carl Edward Rasmussen, Jan Peters +1
Previously, the exploding gradient problem has been explained to be central in deep learning and model-based reinforcement learning, because it causes numerical issues and instabil…
Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks
Martin Trapp, Robert Peharz, Carl E. Rasmussen +1
While Gaussian processes (GPs) are the method of choice for regression tasks, they also come with practical difficulties, as inference cost scales cubic in time and quadratic in me…
Variational Gaussian Process State-Space Models
Roger Frigola, Yutian Chen, Carl E. Rasmussen
State-space models have been successfully used for more than fifty years in different areas of science and engineering. We present a procedure for efficient variational Bayesian le…