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Found 8 papers, 6 papers with code
Random Pareto front surfaces
As a motivating example, we investigate how these statistics can be used within a design of experiments setting, where the goal is to both infer and use the Pareto front surface distribution in order to make effective decisions.
Transition Constrained Bayesian Optimization via Markov Decision Processes
Bayesian optimization is a methodology to optimize black-box functions.
Practical Path-based Bayesian Optimization
There has been a surge in interest in data-driven experimental design with applications to chemical engineering and drug manufacturing.
Multi-objective optimisation via the R2 utilities
As part of our work, we show that these utilities are monotone and submodular set functions which can be optimised effectively using greedy optimisation algorithms.
Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization
Bayesian Optimization is a useful tool for experiment design.
Joint Entropy Search for Multi-objective Bayesian Optimization
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.
Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces
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 or no manual tuning, naturally handle discrete feature spaces, and are relatively insensitive to outliers in the training data.
SnAKe: Bayesian Optimization with Pathwise Exploration
Bayesian Optimization is a very effective tool for optimizing expensive black-box functions.
