Exploiting gradients and Hessians in Bayesian optimization and Bayesian quadrature

31 March 2017

Papers citing "Exploiting gradients and Hessians in Bayesian optimization and Bayesian quadrature"

9 / 9 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
Using Distance Correlation for Efficient Bayesian Optimization T. Kanazawa 48 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 20 12 0 08 Jan 2021
Neural Network Approximations for Calabi-Yau Metrics Vishnu Jejjala D. M. Peña Challenger Mishra 31 53 0 31 Dec 2020
Information Directed Sampling for Linear Partial Monitoring Johannes Kirschner Tor Lattimore Andreas Krause 24 46 0 25 Feb 2020
Tensor Basis Gaussian Process Models of Hyperelastic Materials A. Frankel Reese E. Jones L. Swiler 19 41 0 23 Dec 2019
Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints Nimish Awalgaonkar Ilias Bilionis Xiaoqi Liu P. Karava Athanasios Tzempelikos AI4TS AI4CE 38 2 0 21 Mar 2019
On the positivity and magnitudes of Bayesian quadrature weights Toni Karvonen Motonobu Kanagawa Simo Särkkä 16 12 0 20 Dec 2018
Benchmarking five global optimization approaches for nano-optical shape optimization and parameter reconstruction Philipp‐Immanuel Schneider Xavier Garcia Santiago V. Soltwisch M. Hammerschmidt Sven Burger C. Rockstuhl 19 88 0 18 Sep 2018