13 citations · 41 across the 13 of their papers we have counts for
13 papers
Meta-learning for sample-efficient Bayesian optimisation of fed-batch processes
Becky Langdon, Gabriel D. Patrón, Chrysoula D. Kappatou +6
The optimisation of fed-batch (bio)chemical process recipes is subject to inherent, underlying, and unmeasurable fluctuations across batches, whose trajectories are difficult to mo…
BARK: A Fully Bayesian Tree Kernel for Black-box Optimization
Toby Boyne, Jose Pablo Folch, Robert M Lee +2
We perform Bayesian optimization using a Gaussian process perspective on Bayesian Additive Regression Trees (BART). Our BART Kernel (BARK) uses tree agreement to define a posterior…
BoFire: Bayesian Optimization Framework Intended for Real Experiments
Johannes P. Dürholt, Thomas S. Asche, Johanna Kleinekorte +15
Our open-source Python package BoFire combines Bayesian Optimization (BO) with other design of experiments (DoE) strategies focusing on developing and optimizing new chemistry. Pre…
Random Pareto front surfaces
Ben Tu, Nikolas Kantas, Robert M. Lee +1
The goal of multi-objective optimisation is to identify the Pareto front surface which is the set obtained by connecting the best trade-off points. Typically this surface is comput…
System-Aware Neural ODE Processes for Few-Shot Bayesian Optimization
Jixiang Qing, Becky D Langdon, Robert M Lee +4
We consider the problem of optimizing initial conditions and termination time in dynamical systems governed by unknown ordinary differential equations (ODEs), where evaluating diff…
Scalarisation-based risk concepts for robust multi-objective optimisation
Ben Tu, Nikolas Kantas, Robert M. Lee +1
Robust optimisation is a well-established framework for optimising functions in the presence of uncertainty. The inherent goal of this problem is to identify a collection of inputs…
Transition Constrained Bayesian Optimization via Markov Decision Processes
Jose Pablo Folch, Calvin Tsay, Robert M Lee +6
Bayesian optimization is a methodology to optimize black-box functions. Traditionally, it focuses on the setting where you can arbitrarily query the search space. However, many rea…
Practical Path-based Bayesian Optimization
Jose Pablo Folch, James Odgers, Shiqiang Zhang +6
There has been a surge in interest in data-driven experimental design with applications to chemical engineering and drug manufacturing. Bayesian optimization (BO) has proven to be…
Multi-objective optimisation via the R2 utilities
Ben Tu, Nikolas Kantas, Robert M. Lee +1
The goal of multi-objective optimisation is to identify a collection of points which describe the best possible trade-offs between the multiple objectives. In order to solve this v…
Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization
Jose Pablo Folch, Robert M Lee, Behrang Shafei +4
Bayesian Optimization is a useful tool for experiment design. Unfortunately, the classical, sequential setting of Bayesian Optimization does not translate well into laboratory expe…
Joint Entropy Search for Multi-objective Bayesian Optimization
Ben Tu, Axel Gandy, Nikolas Kantas +1
Many real-world problems can be phrased as a multi-objective optimization problem, where the goal is to identify the best set of compromises between the competing objectives. Multi…
Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces
Alexander Thebelt, Calvin Tsay, Robert M. Lee +4
Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning and neural architecture search, as they achieve good predictive performance with little…
SnAKe: Bayesian Optimization with Pathwise Exploration
Jose Pablo Folch, Shiqiang Zhang, Robert M Lee +5
Bayesian Optimization is a very effective tool for optimizing expensive black-box functions. Inspired by applications developing and characterizing reaction chemistry using droplet…