273 citations · 988 across the 24 of their papers we have counts for
4 papers · 1 filter
Inverse Reinforcement Learning via Deep Gaussian Process
Ming Jin, Andreas Damianou, Pieter Abbeel +1
We propose a new approach to inverse reinforcement learning (IRL) based on the deep Gaussian process (deep GP) model, which is capable of learning complicated reward structures wit…
Recurrent Gaussian Processes
César Lincoln C. Mattos, Zhenwen Dai, Andreas Damianou +3
We define Recurrent Gaussian Processes (RGP) models, a general family of Bayesian nonparametric models with recurrent GP priors which are able to learn dynamical patterns from sequ…
Variational Auto-encoded Deep Gaussian Processes
Zhenwen Dai, Andreas Damianou, Javier González +1
We develop a scalable deep non-parametric generative model by augmenting deep Gaussian processes with a recognition model. Inference is performed in a novel scalable variational fr…
Semi-described and semi-supervised learning with Gaussian processes
Andreas Damianou, Neil D. Lawrence
Propagating input uncertainty through non-linear Gaussian process (GP) mappings is intractable. This hinders the task of training GPs using uncertain and partially observed inputs.…