65 citations · 100 across the 12 of their papers we have counts for
3 papers · 1 filter
Dual Parameterization of Sparse Variational Gaussian Processes
Vincent Adam, Paul E. Chang, Mohammad Emtiyaz Khan +1
Sparse variational Gaussian process (SVGP) methods are a common choice for non-conjugate Gaussian process inference because of their computational benefits. In this paper, we impro…
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…