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Found 5 papers, 4 papers with code
A Quadrature Approach for General-Purpose Batch Bayesian Optimization via Probabilistic Lifting
Parallelisation in Bayesian optimisation is a common strategy but faces several challenges: the need for flexibility in acquisition functions and kernel choices, flexibility dealing with discrete and continuous variables simultaneously, model misspecification, and lastly fast massive parallelisation.
Bayesian Quadrature for Neural Ensemble Search
Ensembling can improve the performance of Neural Networks, but existing approaches struggle when the architecture likelihood surface has dispersed, narrow peaks.
SOBER: Highly Parallel Bayesian Optimization and Bayesian Quadrature over Discrete and Mixed Spaces
Batch Bayesian optimisation and Bayesian quadrature have been shown to be sample-efficient methods of performing optimisation and quadrature where expensive-to-evaluate objective functions can be queried in parallel.
Marginalising over Stationary Kernels with Bayesian Quadrature
Marginalising over families of Gaussian Process kernels produces flexible model classes with well-calibrated uncertainty estimates.
Batch Selection for Parallelisation of Bayesian Quadrature
Integration over non-negative integrands is a central problem in machine learning (e. g. for model averaging, (hyper-)parameter marginalisation, and computing posterior predictive distributions).
