20 citations · 24 across the 5 of their papers we have counts for
9 papers
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning
Paul E. Chang, Prakhar Verma, ST John +3
Gaussian processes (GPs) are the main surrogate functions used for sequential modelling such as Bayesian Optimization and Active Learning. Their drawbacks are poor scaling with dat…
GIBBON: General-purpose Information-Based Bayesian OptimisatioN
Henry B. Moss, David S. Leslie, Javier Gonzalez +1
This paper describes a general-purpose extension of max-value entropy search, a popular approach for Bayesian Optimisation (BO). A novel approximation is proposed for the informati…
Gaussian Process Molecule Property Prediction with FlowMO
Henry B. Moss, Ryan-Rhys Griffiths
We present FlowMO: an open-source Python library for molecular property prediction with Gaussian Processes. Built upon GPflow and RDKit, FlowMO enables the user to make predictions…
BOSS: Bayesian Optimization over String Spaces
Henry B. Moss, Daniel Beck, Javier Gonzalez +2
This article develops a Bayesian optimization (BO) method which acts directly over raw strings, proposing the first uses of string kernels and genetic algorithms within BO loops. R…
BOSH: Bayesian Optimization by Sampling Hierarchically
Henry B. Moss, David S. Leslie, Paul Rayson
Deployments of Bayesian Optimization (BO) for functions with stochastic evaluations, such as parameter tuning via cross validation and simulation optimization, typically optimize a…
MUMBO: MUlti-task Max-value Bayesian Optimization
Henry B. Moss, David S. Leslie, Paul Rayson
We propose MUMBO, the first high-performing yet computationally efficient acquisition function for multi-task Bayesian optimization. Here, the challenge is to perform efficient opt…