Practical Path-based Bayesian Optimization
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Abstract (arXiv)
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 adaptable to such cases, since we can model the reactions of interest as expensive black-box functions. Sometimes, the cost of this black-box functions can be separated into two parts: (a) the cost of the experiment itself, and (b) the cost of changing the input parameters. In this short paper, we extend the SnAKe algorithm to deal with both types of costs simultaneously. We further propose extensions to the case of a maximum allowable input change, as well as to the multi-objective setting.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 11 of its 29 references that are in the map. First posted on arXiv 1 Dec 2023.
Builds on
11 of 29 references.
References · 11
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 603
- 2020Efficiently Sampling Functions from Gaussian Process Posteriorscited by 125
- 2018A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizationscited by 98
- 2022Joint Entropy Search for Multi-objective Bayesian Optimizationcited by 45
- 2016Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimationcited by 40
- 2022Active Exploration via Experiment Design in Markov Chainscited by 19
- 2022SnAKe: Bayesian Optimization with Pathwise Explorationcited by 14
- 2021Multi-Objective Constrained Optimization for Energy Applications via Tree Ensemblescited by 12
- 2022Movement Penalized Bayesian Optimization with Application to Wind Energy Systemscited by 7
- 2023MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learningcited by 5
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Citing papers per year: 2024: 2.
2 papers.