65 citations · 100 across the 12 of their papers we have counts for
12 papers
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…
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…
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…
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era
Nicolas Durrande, Vincent Adam, Lucas Bordeaux +2
Banded matrices can be used as precision matrices in several models including linear state-space models, some Gaussian processes, and Gaussian Markov random fields. The aim of the…
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…
Discrete flow posteriors for variational inference in discrete dynamical systems
Laurence Aitchison, Vincent Adam, Srinivas C. Turaga
Each training step for a variational autoencoder (VAE) requires us to sample from the approximate posterior, so we usually choose simple (e.g. factorised) approximate posteriors in…
Structured Variational Inference for Coupled Gaussian Processes
Vincent Adam
Sparse variational approximations allow for principled and scalable inference in Gaussian Process (GP) models. In settings where several GPs are part of the generative model, these…