Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?

30 January 2023

Papers citing "Are Random Decompositions all we need in High Dimensional Bayesian Optimisation?"

9 / 9 papers shown

Title
Learning Low-Dimensional Embeddings for Black-Box Optimization Riccardo Busetto Manas Mejari Marco Forgione Alberto Bemporad Dario Piga 24 0 0 02 May 2025
Cliqueformer: Model-Based Optimization with Structured Transformers J. Kuba Pieter Abbeel Sergey Levine OffRL AI4CE 62 2 0 17 Oct 2024
A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences Miguel González Duque Richard Michael Simon Bartels Yevgen Zainchkovskyy Søren Hauberg Wouter Boomsma 49 4 0 07 Jun 2024
Vanilla Bayesian Optimization Performs Great in High Dimensions Carl Hvarfner E. Hellsten Luigi Nardi 39 17 0 03 Feb 2024
Relaxing the Additivity Constraints in Decentralized No-Regret High-Dimensional Bayesian Optimization Anthony Bardou Patrick Thiran Thomas Begin 24 4 0 31 May 2023
Misspecified Gaussian Process Bandit Optimization Ilija Bogunovic Andreas Krause 57 43 0 09 Nov 2021
Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization Benjamin Letham Roberto Calandra Akshara Rai E. Bakshy 78 111 0 31 Jan 2020
NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search Arber Zela Julien N. Siems Frank Hutter 88 147 0 28 Jan 2020
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 287 9,167 0 06 Jun 2015