34 citations · 146 across the 19 of their papers we have counts for
20 papers
Dynamic Gaussian Processes and the Vanilla-SPDE Exchange
Rui-Yang Zhang, Lachlan Astfalck, Edward Cripps +2
Gaussian process inference is often limited by cubic computational costs, a challenge that becomes more pronounced in spatio-temporal settings where posterior inference is required…
BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields
Rui-Yang Zhang, Lachlan Astfalck, Edward Cripps +2
We introduce a formal active learning methodology for guiding the placement of Lagrangian observers to infer time-dependent vector fields -- a key task in oceanography, marine scie…
Inducing Point Allocation for Sparse Gaussian Processes in 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; however, we show that existing methods for allocating their inducing points sever…
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
Victor Picheny, Joel Berkeley, Henry B. Moss +13
We present Trieste, an open-source Python package for Bayesian optimization and active learning benefiting from the scalability and efficiency of TensorFlow. Our library enables th…
MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learning
Adam X. Yang, Laurence Aitchison, Henry B. Moss
In Bayesian optimisation, we often seek to minimise the black-box objective functions that arise in real-world physical systems. A primary contributor to the cost of evaluating suc…
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…
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…
Data-Driven Discovery of Molecular Photoswitches with Multioutput Gaussian Processes
Ryan-Rhys Griffiths, Jake L. Greenfield, Aditya R. Thawani +7
Photoswitchable molecules display two or more isomeric forms that may be accessed using light. Separating the electronic absorption bands of these isomers is key to selectively add…
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…
Scalable Thompson Sampling using Sparse Gaussian Process Models
Sattar Vakili, Henry Moss, Artem Artemev +2
Thompson Sampling (TS) from Gaussian Process (GP) models is a powerful tool for the optimization of black-box functions. Although TS enjoys strong theoretical guarantees and convin…
BOFFIN TTS: Few-Shot Speaker Adaptation by Bayesian Optimization
Henry B. Moss, Vatsal Aggarwal, Nishant Prateek +2
We present BOFFIN TTS (Bayesian Optimization For FIne-tuning Neural Text To Speech), a novel approach for few-shot speaker adaptation. Here, the task is to fine-tune a pre-trained…
FIESTA: Fast IdEntification of State-of-The-Art models using adaptive bandit algorithms
Henry B. Moss, Andrew Moore, David S. Leslie +1
We present FIESTA, a model selection approach that significantly reduces the computational resources required to reliably identify state-of-the-art performance from large collectio…
Using J-K fold Cross Validation to Reduce Variance When Tuning NLP Models
Henry B. Moss, David S. Leslie, Paul Rayson
K-fold cross validation (CV) is a popular method for estimating the true performance of machine learning models, allowing model selection and parameter tuning. However, the very pr…