
A versatile and modular framework called SOBER is introduced for batch Bayesian optimization, which leverages probabilistic lifting and kernel quadrature to offer unique benefits such as adaptive batch sizes, robustness against model misspecification, and a natural stopping criterion.


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a-versatile-and-modular-framework-for-batch-bayesian-optimization-via-probabilistic-lifting-and-kernel-quadrature


A Versatile and Modular Framework for Batch Bayesian Optimization via Probabilistic Lifting and Kernel Quadrature


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Minimal Terminal Variance (MTV) is a batch design method that generates an initial batch by optimizing an acquisition function, and uses the same acquisition function to design all batches in a Batch Bayesian Optimization sequence.


optimizing-batch-bayesian-optimization-with-minimal-terminal-variance


Optimizing Batch Bayesian Optimization with Minimal Terminal Variance
