13 citations · 37 across the 13 of their papers we have counts for
5 papers · 1 filter
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…
Adaptive Batch Sizes for Active Learning A Probabilistic Numerics Approach
Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen +4
Active learning parallelization is widely used, but typically relies on fixing the batch size throughout experimentation. This fixed approach is inefficient because of a dynamic tr…
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…
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination
Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen +2
Calculation of Bayesian posteriors and model evidences typically requires numerical integration. Bayesian quadrature (BQ), a surrogate-model-based approach to numerical integration…
Probabilistic Spatial Transformer Networks
Pola Schwöbel, Frederik Warburg, Martin Jørgensen +2
Spatial Transformer Networks (STNs) estimate image transformations that can improve downstream tasks by `zooming in' on relevant regions in an image. However, STNs are hard to trai…