ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2102.09009
  4. Cited By
BORE: Bayesian Optimization by Density-Ratio Estimation

BORE: Bayesian Optimization by Density-Ratio Estimation

17 February 2021
Louis C. Tiao
Aaron Klein
Matthias Seeger
Edwin V. Bonilla
Cédric Archambeau
F. Ramos
ArXivPDFHTML

Papers citing "BORE: Bayesian Optimization by Density-Ratio Estimation"

7 / 7 papers shown
Title
Bayesian Optimization by Kernel Regression and Density-based Exploration
Bayesian Optimization by Kernel Regression and Density-based Exploration
Tansheng Zhu
Hongyu Zhou
Ke Jin
Xusheng Xu
Qiufan Yuan
Lijie Ji
138
0
0
10 Feb 2025
Variational Search Distributions
Variational Search Distributions
Daniel M. Steinberg
Rafael Oliveira
Cheng Soon Ong
Edwin V. Bonilla
33
0
0
10 Sep 2024
On the development of a practical Bayesian optimisation algorithm for
  expensive experiments and simulations with changing environmental conditions
On the development of a practical Bayesian optimisation algorithm for expensive experiments and simulations with changing environmental conditions
Mike Diessner
Kevin J. Wilson
Richard D. Whalley
19
0
0
05 Feb 2024
Density Ratio Estimation-based Bayesian Optimization with
  Semi-Supervised Learning
Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning
Jungtaek Kim
32
1
0
24 May 2023
Max-value Entropy Search for Efficient Bayesian Optimization
Max-value Entropy Search for Efficient Bayesian Optimization
Zi Wang
Stefanie Jegelka
110
403
0
06 Mar 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
276
5,660
0
05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,136
0
06 Jun 2015
1