ICML 2022Spotlight
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times
Authors: Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
Abstract
Computing a Gaussian process (GP) posterior has a computational cost cubical in the number of historical points. A reformulation of the same GP posterior highlights that this complexity mainly depends on how many