Search Results for author: Vincent Dutordoir

Found 14 papers, 5 papers with code

Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes

no code implementations27 Apr 2023 Louis C. Tiao, Vincent Dutordoir, Victor Picheny

Despite their many desirable properties, Gaussian processes (GPs) are often compared unfavorably to deep neural networks (NNs) for lacking the ability to learn representations.

Gaussian Processes

Neural Diffusion Processes

1 code implementation8 Jun 2022 Vincent Dutordoir, Alan Saul, Zoubin Ghahramani, Fergus Simpson

Neural network approaches for meta-learning distributions over functions have desirable properties such as increased flexibility and a reduced complexity of inference.

Bayesian Optimisation Denoising +2

Deep Neural Networks as Point Estimates for Deep Gaussian Processes

no code implementations NeurIPS 2021 Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande

This results in models that can either be seen as neural networks with improved uncertainty prediction or deep Gaussian processes with increased prediction accuracy.

Bayesian Inference Gaussian Processes +1

A Tutorial on Sparse Gaussian Processes and Variational Inference

no code implementations27 Dec 2020 Felix Leibfried, Vincent Dutordoir, ST John, Nicolas Durrande

In this context, a convenient choice for approximate inference is variational inference (VI), where the problem of Bayesian inference is cast as an optimization problem -- namely, to maximize a lower bound of the log marginal likelihood.

Bayesian Inference Gaussian Processes +2

Sparse Gaussian Processes with Spherical Harmonic Features

no code implementations ICML 2020 Vincent Dutordoir, Nicolas Durrande, James Hensman

We introduce a new class of inter-domain variational Gaussian processes (GP) where data is mapped onto the unit hypersphere in order to use spherical harmonic representations.

Gaussian Processes

Scalable Thompson Sampling using Sparse Gaussian Process Models

no code implementations NeurIPS 2021 Sattar Vakili, Henry Moss, Artem Artemev, Vincent Dutordoir, Victor Picheny

We provide theoretical guarantees and show that the drastic reduction in computational complexity of scalable TS can be enjoyed without loss in the regret performance over the standard TS.

Thompson Sampling

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

Bayesian Image Classification with Deep Convolutional Gaussian Processes

no code implementations15 Feb 2019 Vincent Dutordoir, Mark van der Wilk, Artem Artemev, James Hensman

We also demonstrate that our fully Bayesian approach improves on dropout-based Bayesian deep learning methods in terms of uncertainty and marginal likelihood estimates.

Classification Decision Making +5

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