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Found 4 papers, 3 papers with code
Good practices for Bayesian Optimization of high dimensional structured spaces
The increasing availability of structured but high dimensional data has opened new opportunities for optimization.
Preferential Batch Bayesian Optimization
This direction has been mainly driven by the use of BO in machine learning hyper-parameter configuration problems.
Active Learning for Decision-Making from Imbalanced Observational Data
Machine learning can help personalized decision support by learning models to predict individual treatment effects (ITE).
Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observations
Bayesian optimization (BO) is a global optimization strategy designed to find the minimum of an expensive black-box function, typically defined on a compact subset of $\mathcal{R}^d$, by using a Gaussian process (GP) as a surrogate model for the objective.
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