ICML 2022spotlight19 citations
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times
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
BibTeX
@InProceedings{pmlr-v162-calandriello22a,
title = {Scaling {G}aussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times},
author = {Calandriello, Daniele and Carratino, Luigi and Lazaric, Alessandro and Valko, Michal and Rosasco, Lorenzo},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {2523--2541},
year = {2022},
editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v162/calandriello22a/calandriello22a.pdf},
url = {https://proceedings.mlr.press/v162/calandriello22a.html},
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