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02/16/2023
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
We present Trieste, an open-source Python package for Bayesian optimizat...
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10/14/2022
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
As Gaussian processes mature, they are increasingly being deployed as pa...
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06/28/2022
Memory Safe Computations with XLA Compiler
Software packages like TensorFlow and PyTorch are designed to support li...
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09/20/2021
Barely Biased Learning for Gaussian Process Regression
Recent work in scalable approximate Gaussian process regression has disc...
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04/12/2021
GPflux: A Library for Deep Gaussian Processes
We introduce GPflux, a Python library for Bayesian deep learning with a ...
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02/16/2021
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients
We propose a lower bound on the log marginal likelihood of Gaussian proc...
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06/25/2020
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation
Many machine learning models require a training procedure based on runni...
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06/09/2020
Scalable Thompson Sampling using Sparse Gaussian Process Models
Thompson Sampling (TS) with Gaussian Process (GP) models is a powerful t...
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03/02/2020
A Framework for Interdomain and Multioutput Gaussian Processes
One obstacle to the use of Gaussian processes (GPs) in large-scale probl...
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01/15/2020
Doubly Sparse Variational Gaussian Processes
The use of Gaussian process models is typically limited to datasets with...
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12/05/2019
Ordinal Bayesian Optimisation
Bayesian optimisation is a powerful tool to solve expensive black-box pr...
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02/15/2019
