Search Results for author: Vincent Adam

Found 11 papers, 4 papers with code

Dual Parameterization of Sparse Variational Gaussian Processes

1 code implementation NeurIPS 2021 Vincent Adam, Paul E. Chang, Mohammad Emtiyaz Khan, Arno Solin

Sparse variational Gaussian process (SVGP) methods are a common choice for non-conjugate Gaussian process inference because of their computational benefits.

Gaussian Processes

Sparse Gaussian Processes for Stochastic Differential Equations

no code implementations NeurIPS Workshop DLDE 2021 Prakhar Verma, Vincent Adam, Arno Solin

We frame the problem of learning stochastic differential equations (SDEs) from noisy observations as an inference problem and aim to maximize the marginal likelihood of the observations in a joint model of the latent paths and the noisy observations.

Gaussian Processes Variational Inference

Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow

2 code implementations26 Mar 2021 John McLeod, Hrvoje Stojic, Vincent Adam, Dongho Kim, Jordi Grau-Moya, Peter Vrancx, Felix Leibfried

This paves the way for new research directions, e. g. investigating uncertainty-aware environment models that are not necessarily neural-network-based, or developing algorithms to solve industrially-motivated benchmarks that share characteristics with real-world problems.

Model-based Reinforcement Learning reinforcement-learning

Sparse Algorithms for Markovian Gaussian Processes

1 code implementation19 Mar 2021 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.

Bayesian Inference Gaussian Processes +2

A Framework for Interdomain and Multioutput Gaussian Processes

1 code implementation2 Mar 2020 Mark van der Wilk, Vincent Dutordoir, ST John, Artem Artemev, Vincent Adam, James Hensman

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 small variations in the model or inference.

Gaussian Processes

Doubly Sparse Variational Gaussian Processes

no code implementations15 Jan 2020 Vincent Adam, Stefanos Eleftheriadis, Nicolas Durrande, Artem Artemev, James Hensman

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.

Gaussian Processes

Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era

no code implementations26 Feb 2019 Nicolas Durrande, Vincent Adam, Lucas Bordeaux, Stefanos Eleftheriadis, James Hensman

Banded matrices can be used as precision matrices in several models including linear state-space models, some Gaussian processes, and Gaussian Markov random fields.

Gaussian Processes Variational Inference

Scalable GAM using sparse variational Gaussian processes

no code implementations28 Dec 2018 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 linear models.

Additive models Gaussian Processes +1

Discrete flow posteriors for variational inference in discrete dynamical systems

no code implementations ICLR 2019 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 which sampling is an efficient computation that fully exploits GPU parallelism.

Variational Inference

Structured Variational Inference for Coupled Gaussian Processes

no code implementations3 Nov 2017 Vincent Adam

Here, we extend previous sparse GP approximations and propose a novel parameterization of variational posteriors in the multi-GP setting allowing for fast and scalable inference capturing posterior dependencies.

Gaussian Processes Variational Inference

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