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

6 June 2022

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

6 / 6 papers shown

Title
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow Victor Picheny Joel Berkeley Henry B. Moss Hrvoje Stojić Uri Granta ... Sergio Pascual-Diaz Stratis Markou Jixiang Qing Nasrulloh Loka Ivo Couckuyt 26 17 0 16 Feb 2023
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation Henry B. Moss Sebastian W. Ober Victor Picheny 32 25 0 24 Jan 2023
GAUCHE: A Library for Gaussian Processes in Chemistry Ryan-Rhys Griffiths Leo Klarner Henry B. Moss Aditya Ravuri Sang T. Truong ... A. Lee Bingqing Cheng Alán Aspuru-Guzik P. Schwaller Jian Tang GP 30 40 0 06 Dec 2022
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning Paul E. Chang Prakhar Verma S. T. John Victor Picheny Henry B. Moss Arno Solin GP 33 6 0 02 Nov 2022
Max-value Entropy Search for Efficient Bayesian Optimization Zi Wang Stefanie Jegelka 110 404 0 06 Mar 2017
Determinantal point processes for machine learning Alex Kulesza B. Taskar 176 1,125 0 25 Jul 2012