13 citations · 41 across the 13 of their papers we have counts for
5 papers · 1 filter
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…