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Exploiting Transitivity for Top-k Selection with Score-Based Dueling Bandits
We extend this to selection problems where sampling results contain quantitative information by proposing a Thurstonian style model and adapting the Pairwise Optimal Computing Budget Allocation for subset selection (POCBAm) sampling method to exploit this model for efficient sample selection.
Practical Bayesian Optimization of Objectives with Conditioning Variables
Bayesian optimization is a class of data efficient model based algorithms typically focused on global optimization.
Efficient and Scalable Batch Bayesian Optimization Using K-Means
We present K-Means Batch Bayesian Optimization (KMBBO), a novel batch sampling algorithm for Bayesian Optimization (BO).
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