65 citations · 73 across the 4 of their papers we have counts for
5 papers
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation
Victor Picheny, Vincent Dutordoir, Artem Artemev +1
Many machine learning models require a training procedure based on running stochastic gradient descent. A key element for the efficiency of those algorithms is the choice of the le…
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…
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…
Ordinal Bayesian Optimisation
Victor Picheny, Sattar Vakili, Artem Artemev
Bayesian optimisation is a powerful tool to solve expensive black-box problems, but fails when the stationary assumption made on the objective function is strongly violated, which…
Bayesian Image Classification with Deep Convolutional Gaussian Processes
Vincent Dutordoir, Mark van der Wilk, Artem Artemev +1
In decision-making systems, it is important to have classifiers that have calibrated uncertainties, with an optimisation objective that can be used for automated model selection an…