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Found 3 papers, 2 papers with code
Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need
We leverage multilevel Monte Carlo (MLMC) to improve the performance of multi-step look-ahead Bayesian optimization (BO) methods that involve nested expectations and maximizations.
Sparse online variational Bayesian regression
Furthermore, for p unknown covariates the method can be implemented exactly online with a cost of $O(p^3)$ in computation and $O(p^2)$ in memory per iteration -- in other words, the cost per iteration is independent of n, and in principle infinite data can be considered.
Fast Deep Mixtures of Gaussian Process Experts
Mixtures of experts have become an indispensable tool for flexible modelling in a supervised learning context, allowing not only the mean function but the entire density of the output to change with the inputs.
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