2 citations · 4 across the 5 of their papers we have counts for
5 papers
A Quadrature Approach for General-Purpose Batch Bayesian Optimization via Probabilistic Lifting
Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen +3
Parallelisation in Bayesian optimisation is a common strategy but faces several challenges: the need for flexibility in acquisition functions and kernel choices, flexibility dealin…
Bayesian Quadrature for Neural Ensemble Search
Saad Hamid, Xingchen Wan, Martin Jørgensen +2
Ensembling can improve the performance of Neural Networks, but existing approaches struggle when the architecture likelihood surface has dispersed, narrow peaks. Furthermore, exist…
SOBER: Highly Parallel Bayesian Optimization and Bayesian Quadrature over Discrete and Mixed Spaces
Masaki Adachi, Satoshi Hayakawa, Saad Hamid +3
Batch Bayesian optimisation and Bayesian quadrature have been shown to be sample-efficient methods of performing optimisation and quadrature where expensive-to-evaluate objective f…
Marginalising over Stationary Kernels with Bayesian Quadrature
Saad Hamid, Sebastian Schulze, Michael A. Osborne +1
Marginalising over families of Gaussian Process kernels produces flexible model classes with well-calibrated uncertainty estimates. Existing approaches require likelihood evaluatio…
Batch Selection for Parallelisation of Bayesian Quadrature
Ed Wagstaff, Saad Hamid, Michael Osborne
Integration over non-negative integrands is a central problem in machine learning (e.g. for model averaging, (hyper-)parameter marginalisation, and computing posterior predictive d…