65 citations · 96 across the 8 of their papers we have counts for
4 papers · 1 filter
Variational Gaussian Process Diffusion Processes
Prakhar Verma, Vincent Adam, Arno Solin
Diffusion processes are a class of stochastic differential equations (SDEs) providing a rich family of expressive models that arise naturally in dynamic modelling tasks. Probabilis…
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow
John McLeod, Hrvoje Stojic, Vincent Adam +4
In the past decade, model-free reinforcement learning (RL) has provided solutions to challenging domains such as robotics. Model-based RL shows the prospect of being more sample-ef…
Disentangled Skill Embeddings for Reinforcement Learning
Janith C. Petangoda, Sergio Pascual-Diaz, Vincent Adam +2
We propose a novel framework for multi-task reinforcement learning (MTRL). Using a variational inference formulation, we learn policies that generalize across both changing dynamic…
Scalable GAM using sparse variational Gaussian processes
Vincent Adam, Nicolas Durrande, ST John
Generalized additive models (GAMs) are a widely used class of models of interest to statisticians as they provide a flexible way to design interpretable models of data beyond linea…