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04/27/2023
Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes
Despite their many desirable properties, Gaussian processes (GPs) are of...
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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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01/24/2023
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation
Sparse Gaussian Processes are a key component of high-throughput Bayesia...
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11/02/2022
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning
Gaussian processes (GPs) are the main surrogate functions used for seque...
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06/27/2022
A penalisation method for batch multi-objective Bayesian optimisation with application in heat exchanger design
We present HIghly Parallelisable Pareto Optimisation (HIPPO) – a batch a...
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06/06/2022
Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation
Sparse Gaussian Processes are a key component of high-throughput Bayesia...
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03/30/2021
Revisiting Bayesian Optimization in the light of the COCO benchmark
It is commonly believed that Bayesian optimization (BO) algorithms are h...
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01/18/2021
TREGO: a Trust-Region Framework for Efficient Global Optimization
Efficient Global Optimization (EGO) is the canonical form of Bayesian op...
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09/15/2020
On Information Gain and Regret Bounds in Gaussian Process Bandits
Consider the sequential optimization of an expensive to evaluate and pos...
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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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02/12/2020
Regret Bounds for Noise-Free Bayesian Optimization
Bayesian optimisation is a powerful method for non-convex black-box opti...
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01/12/2020
Bayesian Quantile and Expectile Optimisation
Bayesian optimisation is widely used to optimise stochastic black box fu...
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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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08/29/2019
Modeling and Optimization with Gaussian Processes in Reduced Eigenbases – Extended Version
Parametric shape optimization aims at minimizing an objective function f...
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04/17/2019
X-Armed Bandits: Optimizing Quantiles and Other Risks
We propose and analyze StoROO, an algorithm for risk optimization on sto...
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02/18/2019
The Kalai-Smorodinski solution for many-objective Bayesian optimization
An ongoing aim of research in multiobjective Bayesian optimization is to...
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01/23/2019
A Review on Quantile Regression for Stochastic Computer Experiments
We report on an empirical study of the main strategies for conditional q...
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11/09/2018
Targeting Solutions in Bayesian Multi-Objective Optimization: Sequential and Parallel Versions
Multi-objective optimization aims at finding trade-off solutions to conf...
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09/27/2018
Budgeted Multi-Objective Optimization with a Focus on the Central Part of the Pareto Front - Extended Version
Optimizing nonlinear systems involving expensive (computer) experiments ...
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11/08/2016
A Bayesian optimization approach to find Nash equilibria
Game theory finds nowadays a broad range of applications in engineering ...
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05/31/2016