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?"

15 / 15 papers shown

Title
Cliqueformer: Model-Based Optimization with Structured Transformers J. Kuba Pieter Abbeel Sergey Levine OffRL AI4CE 93 3 0 17 Oct 2024
Additive Gaussian Processes Revisited Xiaoyu Lu A. Boukouvalas J. Hensman 23 22 0 20 Jun 2022
LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso Kenan Sehic Alexandre Gramfort Joseph Salmon Luigi Nardi 60 38 0 04 Nov 2021
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning Antoine Grosnit Rasul Tutunov A. Maraval Ryan-Rhys Griffiths Alexander I. Cowen-Rivers ... Wenlong Lyu Zhitang Chen Jun Wang Jan Peters Haitham Bou-Ammar BDL DRL 60 60 0 07 Jun 2021
Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020 Ryan Turner David Eriksson M. McCourt J. Kiili Eero Laaksonen Zhen Xu Isabelle M Guyon BDL 50 297 0 20 Apr 2021
Good practices for Bayesian Optimization of high dimensional structured spaces E. Siivola Javier I. González Andrei Paleyes Aki Vehtari 67 37 0 31 Dec 2020
HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation Alexander I. Cowen-Rivers Wenlong Lyu Rasul Tutunov Zhi Wang Antoine Grosnit ... A. Maraval Hao Jianye Jun Wang Jan Peters H. Ammar 66 77 0 07 Dec 2020
Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization Benjamin Letham Roberto Calandra Akshara Rai E. Bakshy 93 114 0 31 Jan 2020
Scalable Global Optimization via Local Bayesian Optimization Samyam Rajbhandari Michael Pearce Jacob R. Gardner Ryan D. Turner Matthias Poloczek 81 463 0 03 Oct 2019
Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces Johannes Kirschner Mojmír Mutný N. Hiller R. Ischebeck Andreas Krause 58 149 0 08 Feb 2019
High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups Paul Rolland Jonathan Scarlett Ilija Bogunovic Volkan Cevher 61 115 0 20 Feb 2018
Automatic chemical design using a data-driven continuous representation of molecules Rafael Gómez-Bombarelli Jennifer N. Wei David Duvenaud José Miguel Hernández-Lobato Benjamín Sánchez-Lengeling Dennis Sheberla J. Aguilera-Iparraguirre Timothy D. Hirzel Ryan P. Adams Alán Aspuru-Guzik 3DV 147 2,921 0 07 Oct 2016
Deep Kernel Learning A. Wilson Zhiting Hu Ruslan Salakhutdinov Eric Xing BDL 228 885 0 06 Nov 2015
Additive Gaussian Processes David Duvenaud H. Nickisch C. Rasmussen GP 94 329 0 19 Dec 2011
Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design Niranjan Srinivas Andreas Krause Sham Kakade Matthias Seeger 144 1,618 0 21 Dec 2009