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Information-theoretic Inducing Point Placement for High-throughput
Bayesian Optimisation

v1v2 (latest)

Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation

6 June 2022

Sebastian W. Ober

ArXiv (abs)PDF HTML Github (248★)

Papers citing "Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation"

4 / 4 papers shown

Trieste: Efficiently Exploring The Depths of Black-box Functions with
TensorFlow

Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow

...

Sergio Pascual-Diaz

264

24

0

16 Feb 2023

Inducing Point Allocation for Sparse Gaussian Processes in
High-Throughput Bayesian Optimisation

Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian OptimisationInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023

Sebastian W. Ober

462

38

0

24 Jan 2023

GAUCHE: A Library for Gaussian Processes in Chemistry

GAUCHE: A Library for Gaussian Processes in ChemistryNeural Information Processing Systems (NeurIPS), 2022

Ryan-Rhys Griffiths

...

Alán Aspuru-Guzik

462

57

0

06 Dec 2022

Fantasizing with Dual GPs in Bayesian Optimization and Active Learning

Fantasizing with Dual GPs in Bayesian Optimization and Active Learning

Arno Solin

295

8

0

02 Nov 2022

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