7 citations · 14 across the 3 of their papers we have counts for
4 papers
Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Need
Shangda Yang, Vitaly Zankin, Maximilian Balandat +4
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
Kody J. H. Law, Vitaly Zankin
This work considers variational Bayesian inference as an inexpensive and scalable alternative to a fully Bayesian approach in the context of sparsity-promoting priors. In particula…
Fast Deep Mixtures of Gaussian Process Experts
Clement Etienam, Kody Law, Sara Wade +1
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 out…
Gradient Descent-based D-optimal Design for the Least-Squares Polynomial Approximation
V. P. Zankin, G. V. Ryzhakov, I. V. Oseledets
In this work, we propose a novel sampling method for Design of Experiments. This method allows to sample such input values of the parameters of a computational model for which the…