Input Warping for Bayesian Optimization of Non-stationary Functions

5 February 2014

Papers citing "Input Warping for Bayesian Optimization of Non-stationary Functions"

50 / 52 papers shown

Title
From Automation to Autonomy in Smart Manufacturing: A Bayesian Optimization Framework for Modeling Multi-Objective Experimentation and Sequential Decision Making Avijit Saha Asru H. Khosravi I. Imtiaz Ahmed Abdullahil Azeem 176 0 0 05 Apr 2025
SEEK: Self-adaptive Explainable Kernel For Nonstationary Gaussian Processes Nima Negarandeh Carlos Mora Ramin Bostanabad 55 0 0 18 Mar 2025
Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization Zeyuan Ma Hongshu Guo Yue-jiao Gong Jun Zhang Kay Chen Tan 119 2 0 01 Nov 2024
Bayesian Optimization with Adaptive Kernels for Robot Control Ruben Martinez-Cantin 14 37 0 10 Feb 2024
Vanilla Bayesian Optimization Performs Great in High Dimensions Carl Hvarfner E. Hellsten Luigi Nardi 34 17 0 03 Feb 2024
Tailoring Mixup to Data for Calibration Quentin Bouniot Pavlo Mozharovskyi Florence dÁlché-Buc 61 1 0 02 Nov 2023
Mixtures of Gaussian process experts based on kernel stick-breaking processes Yuji Saikai Khue-Dung Dang 17 0 0 26 Apr 2023
Unleashing the Potential of Acquisition Functions in High-Dimensional Bayesian Optimization Jiayu Zhao Renyu Yang Shenghao Qiu Zheng Wang 16 4 0 16 Feb 2023
Non-Gaussian Process Regression Y. Kindap S. Godsill GP 13 1 0 07 Sep 2022
Bayesian Optimization with Informative Covariance Afonso Eduardo Michael U. Gutmann 24 3 0 04 Aug 2022
Heuristic-free Optimization of Force-Controlled Robot Search Strategies in Stochastic Environments Bastian Alt Darko Katic Rainer Jäkel Michael Beetz 23 6 0 15 Jul 2022
Recent Advances in Bayesian Optimization Xilu Wang Yaochu Jin Sebastian Schmitt Markus Olhofer 40 200 0 07 Jun 2022
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification Juan Maroñas Daniel Hernández-Lobato 19 6 0 30 May 2022
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes Felix Jimenez Matthias Katzfuss 21 10 0 02 Mar 2022
Adaptive Gradient Methods with Local Guarantees Zhou Lu Wenhan Xia Sanjeev Arora Elad Hazan ODL 27 9 0 02 Mar 2022
B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search Hyunghun Cho Jungwook Shin Wonjong Rhee 33 7 0 07 Feb 2022
Online Calibrated and Conformal Prediction Improves Bayesian Optimization Shachi Deshpande Charles Marx Volodymyr Kuleshov 13 7 0 08 Dec 2021
Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces Sam Daulton David Eriksson Maximilian Balandat E. Bakshy 22 105 0 22 Sep 2021
Non-smooth Bayesian Optimization in Tuning Problems Hengrui Luo J. Demmel Younghyun Cho Xin Li Yang Liu 25 13 0 15 Sep 2021
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges B. Bischl Martin Binder Michel Lang Tobias Pielok Jakob Richter ... Theresa Ullmann Marc Becker A. Boulesteix Difan Deng Marius Lindauer 85 455 0 13 Jul 2021
Bag of Baselines for Multi-objective Joint Neural Architecture Search and Hyperparameter Optimization Julia Guerrero-Viu Sven Hauns Sergio Izquierdo Guilherme Miotto Simon Schrodi André Biedenkapp T. Elsken Difan Deng Marius Lindauer Frank Hutter AI4CE 17 25 0 03 May 2021
Revisiting Bayesian Optimization in the light of the COCO benchmark Rodolphe Le Riche Victor Picheny 16 26 0 30 Mar 2021
Learning to Optimize: A Primer and A Benchmark Tianlong Chen Xiaohan Chen Wuyang Chen Howard Heaton Jialin Liu Zhangyang Wang W. Yin 54 225 0 23 Mar 2021
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization Valerio Perrone Huibin Shen Aida Zolic I. Shcherbatyi Amr Ahmed ... Barbara Pogorzelska Miroslav Miladinovic K. Kenthapadi Matthias Seeger Cédric Archambeau 45 16 0 15 Dec 2020
Neural fidelity warping for efficient robot morphology design Sha Hu Zeshi Yang Greg Mori 55 4 0 08 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 27 74 0 07 Dec 2020
Scalable Constrained Bayesian Optimization David Eriksson Matthias Poloczek 33 95 0 20 Feb 2020
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization Maximilian Balandat Brian Karrer Daniel R. Jiang Sam Daulton Benjamin Letham A. Wilson E. Bakshy 32 93 0 14 Oct 2019
Scalable Global Optimization via Local Bayesian Optimization Samyam Rajbhandari Michael Pearce Jacob R. Gardner Ryan D. Turner Matthias Poloczek 48 450 0 03 Oct 2019
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning Valerio Perrone Huibin Shen Matthias Seeger Cédric Archambeau Rodolphe Jenatton 27 96 0 27 Sep 2019
Reducing The Search Space For Hyperparameter Optimization Using Group Sparsity Minsu Cho C. Hegde 19 11 0 24 Apr 2019
An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning Yaoyao Liu Bernt Schiele Qianru Sun BDL 38 128 0 17 Apr 2019
Fast Efficient Hyperparameter Tuning for Policy Gradients Supratik Paul Vitaly Kurin Shimon Whiteson 22 32 0 18 Feb 2019
Multi-level CNN for lung nodule classification with Gaussian Process assisted hyperparameter optimization Miao Zhang Huiqi Li Juan Lyu S. Ling Steven W. Su AI4CE 17 11 0 02 Jan 2019
Neural Non-Stationary Spectral Kernel Sami Remes Markus Heinonen Samuel Kaski BDL 16 9 0 27 Nov 2018
A Tutorial on Bayesian Optimization P. Frazier GP 15 1,737 0 08 Jul 2018
A unified strategy for implementing curiosity and empowerment driven reinforcement learning Ildefons Magrans de Abril Ryota Kanai 17 18 0 18 Jun 2018
BOCK : Bayesian Optimization with Cylindrical Kernels Changyong Oh E. Gavves Max Welling 23 135 0 05 Jun 2018
Bayesian Alignments of Warped Multi-Output Gaussian Processes Markus Kaiser Clemens Otte Thomas Runkler Carl Henrik Ek 24 19 0 08 Oct 2017
Hyperparameter Optimization: A Spectral Approach Elad Hazan Adam R. Klivans Yang Yuan 27 118 0 02 Jun 2017
Practical heteroskedastic Gaussian process modeling for large simulation experiments M. Binois R. Gramacy M. Ludkovski 30 181 0 17 Nov 2016
Learning to Learn without Gradient Descent by Gradient Descent Yutian Chen Matthew W. Hoffman Sergio Gomez Colmenarejo Misha Denil Timothy Lillicrap Matt Botvinick Nando de Freitas 21 42 0 11 Nov 2016
Differentially Private Gaussian Processes M. Smith Max Zwiessele Neil D. Lawrence 13 15 0 02 Jun 2016
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets Aaron Klein Stefan Falkner Simon Bartels Philipp Hennig Frank Hutter AI4CE 35 545 0 23 May 2016
Online Optimization of Smoothed Piecewise Constant Functions Vincent Cohen-Addad Varun Kanade 17 25 0 07 Apr 2016
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo Markus Heinonen Henrik Mannerstrom Juho Rousu Samuel Kaski Harri Lähdesmäki 25 102 0 18 Aug 2015
Non-stochastic Best Arm Identification and Hyperparameter Optimization Kevin G. Jamieson Ameet Talwalkar 47 565 0 27 Feb 2015
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention Ke Xu Jimmy Ba Ryan Kiros Kyunghyun Cho Aaron Courville Ruslan Salakhutdinov R. Zemel Yoshua Bengio DiffM 121 10,008 0 10 Feb 2015
Multi-task Neural Networks for QSAR Predictions George E. Dahl Navdeep Jaitly Ruslan Salakhutdinov 62 277 0 04 Jun 2014
Manifold Gaussian Processes for Regression Roberto Calandra Jan Peters C. Rasmussen M. Deisenroth 92 271 0 24 Feb 2014