Bayesian Optimization with Gradients

13 March 2017

Papers citing "Bayesian Optimization with Gradients"

30 / 30 papers shown

Title
Batch Active Learning in Gaussian Process Regression using Derivatives Hon Sum Alec Yu Christoph Zimmer D. Nguyen-Tuong GP 31 1 0 03 Aug 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 44 4 0 07 Jun 2024
Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes Hao Zhao Rong Pan 16 1 0 05 Dec 2023
Robust and Conjugate Gaussian Process Regression Matias Altamirano F. Briol Jeremias Knoblauch 28 10 0 01 Nov 2023
Generating Transferable Adversarial Simulation Scenarios for Self-Driving via Neural Rendering Yasasa Abeysirigoonawardena Kevin Xie Chuhan Chen Salar Hosseini Ruiting Chen Ruiqi Wang Florian Shkurti 36 2 0 27 Sep 2023
Gaussian Max-Value Entropy Search for Multi-Agent Bayesian Optimization Haitong Ma Tianpeng Zhang Yixuan Wu Flavio du Pin Calmon Na Li 26 10 0 10 Mar 2023
Scalable Bayesian optimization with high-dimensional outputs using randomized prior networks Mohamed Aziz Bhouri M. Joly Robert Yu S. Sarkar P. Perdikaris BDL UQCV AI4CE 19 1 0 14 Feb 2023
Global Optimization with Parametric Function Approximation Chong Liu Yu-Xiang Wang 36 7 0 16 Nov 2022
Efficient computation of the Knowledge Gradient for Bayesian Optimization Juan Ungredda Michael Pearce Juergen Branke 35 2 0 30 Sep 2022
Inference of Regulatory Networks Through Temporally Sparse Data Mohammad Alali Mahdi Imani 22 17 0 21 Jul 2022
Thinking inside the box: A tutorial on grey-box Bayesian optimization Raul Astudillo P. Frazier 20 35 0 02 Jan 2022
A portfolio approach to massively parallel Bayesian optimization M. Binois Nicholson T. Collier J. Ozik 27 9 0 18 Oct 2021
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO Katharina Eggensperger Philip Muller Neeratyoy Mallik Matthias Feurer René Sass Aaron Klein Noor H. Awad Marius Lindauer Frank Hutter 46 100 0 14 Sep 2021
Scaling Gaussian Processes with Derivative Information Using Variational Inference Misha Padidar Xinran Zhu Leo Huang Jacob R. Gardner D. Bindel BDL 19 18 0 08 Jul 2021
Using Distance Correlation for Efficient Bayesian Optimization T. Kanazawa 44 3 0 17 Feb 2021
Bayesian optimization with improved scalability and derivative information for efficient design of nanophotonic structures Xavier Garcia Santiago Sven Burger C. Rockstuhl Philipp‐Immanuel Schneider 15 12 0 08 Jan 2021
Time series forecasting with Gaussian Processes needs priors Giorgio Corani A. Benavoli Marco Zaffalon GP AI4TS 13 27 0 17 Sep 2020
Sherpa: Robust Hyperparameter Optimization for Machine Learning L. Hertel Julian Collado Peter Sadowski J. Ott Pierre Baldi 86 103 0 08 May 2020
Information Directed Sampling for Linear Partial Monitoring Johannes Kirschner Tor Lattimore Andreas Krause 16 46 0 25 Feb 2020
Static and Dynamic Values of Computation in MCTS Eren Sezener Peter Dayan 14 5 0 11 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 36 450 0 03 Oct 2019
BISTRO: Berkeley Integrated System for Transportation Optimization Sidney A. Feygin Jessica R. Lazarus E. Forscher Valentine Golfier-Vetterli Jonathan W. Lee Abhishek Gupta Rashid A. Waraich C. Sheppard Alexandre M. Bayen 17 8 0 10 Aug 2019
Accelerating Experimental Design by Incorporating Experimenter Hunches Cheng Li Santu Rana Sunil R. Gupta Vu Nguyen Svetha Venkatesh ... David Rubín de Celis Leal Teo Slezak Murray Height M. Mohammed I. Gibson 24 33 0 22 Jul 2019
Knowledge Gradient for Selection with Covariates: Consistency and Computation Liang Ding L. Hong Haihui Shen Xiaowei Zhang BDL 13 27 0 12 Jun 2019
Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints Nimish Awalgaonkar Ilias Bilionis Xiaoqi Liu P. Karava Athanasios Tzempelikos AI4TS AI4CE 27 2 0 21 Mar 2019
Scaling Gaussian Process Regression with Derivatives David Eriksson Kun Dong E. Lee D. Bindel A. Wilson GP 14 75 0 29 Oct 2018
A Tutorial on Bayesian Optimization P. Frazier GP 13 1,737 0 08 Jul 2018
Maximizing acquisition functions for Bayesian optimization James T. Wilson Frank Hutter M. Deisenroth 46 240 0 25 May 2018
Multi-Information Source Optimization Matthias Poloczek Jialei Wang P. Frazier 41 198 0 01 Mar 2016