34 citations · 87 across the 14 of their papers we have counts for
5 papers · 1 filter
GAUCHE: A Library for Gaussian Processes in Chemistry
Ryan-Rhys Griffiths, Leo Klarner, Henry B. Moss +24
We introduce GAUCHE, a library for GAUssian processes in CHEmistry. Gaussian processes have long been a cornerstone of probabilistic machine learning, affording particular advantag…
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…
Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation
Henry B. Moss, Sebastian W. Ober, Victor Picheny
Sparse Gaussian Processes are a key component of high-throughput Bayesian optimisation (BO) loops -- an increasingly common setting where evaluation budgets are large and highly pa…
A penalisation method for batch multi-objective Bayesian optimisation with application in heat exchanger design
Andrei Paleyes, Henry B. Moss, Victor Picheny +2
We present HIghly Parallelisable Pareto Optimisation (HIPPO) -- a batch acquisition function that enables multi-objective Bayesian optimisation methods to efficiently exploit paral…
ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimization
Jixiang Qing, Henry B. Moss, Tom Dhaene +1
We present Parallel Feasible Pareto Frontier Entropy Search (ES) -- a novel information-theoretic acquisition function for multi-objective Bayesian optimization su…