65 citations · 96 across the 8 of their papers we have counts for
10 papers
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…
Sparse Algorithms for Markovian Gaussian Processes
William J. Wilkinson, Arno Solin, Vincent Adam
Approximate Bayesian inference methods that scale to very large datasets are crucial in leveraging probabilistic models for real-world time series. Sparse Markovian Gaussian proces…
A Framework for Interdomain and Multioutput Gaussian Processes
Mark van der Wilk, Vincent Dutordoir, ST John +3
One obstacle to the use of Gaussian processes (GPs) in large-scale problems, and as a component in deep learning system, is the need for bespoke derivations and implementations for…
Non-linear regression models for behavioral and neural data analysis
Vincent Adam, Alexandre Hyafil
Regression models are popular tools in empirical sciences to infer the influence of a set of variables onto a dependent variable given an experimental dataset. In neuroscience and…
Doubly Sparse Variational Gaussian Processes
Vincent Adam, Stefanos Eleftheriadis, Nicolas Durrande +2
The use of Gaussian process models is typically limited to datasets with a few tens of thousands of observations due to their complexity and memory footprint. The two most commonly…
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…