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A supermartingale approach to Gaussian process based sequential design
  of experiments

A supermartingale approach to Gaussian process based sequential design of experiments

3 August 2016
Julien Bect
F. Bachoc
D. Ginsbourger
ArXivPDFHTML

Papers citing "A supermartingale approach to Gaussian process based sequential design of experiments"

14 / 14 papers shown
Title
Bayesian Optimization for Non-Convex Two-Stage Stochastic Optimization Problems
Bayesian Optimization for Non-Convex Two-Stage Stochastic Optimization Problems
Jack M. Buckingham
Ivo Couckuyt
Juergen Branke
40
0
0
30 Aug 2024
Computing conservative probabilities of rare events with surrogates
Computing conservative probabilities of rare events with surrogates
Nicolas Bousquet
30
0
0
26 Mar 2024
Gaussian Processes on Distributions based on Regularized Optimal
  Transport
Gaussian Processes on Distributions based on Regularized Optimal Transport
F. Bachoc
Louis Bethune
Alberto González Sanz
Jean-Michel Loubes
GP
OT
34
7
0
12 Oct 2022
Bayesian Optimization of Function Networks
Bayesian Optimization of Function Networks
Raul Astudillo
P. Frazier
32
36
0
31 Dec 2021
Continuous logistic Gaussian random measure fields for spatial
  distributional modelling
Continuous logistic Gaussian random measure fields for spatial distributional modelling
Athénais Gautier
D. Ginsbourger
19
0
0
06 Oct 2021
Uncertainty Quantification and Experimental Design for Large-Scale
  Linear Inverse Problems under Gaussian Process Priors
Uncertainty Quantification and Experimental Design for Large-Scale Linear Inverse Problems under Gaussian Process Priors
Cédric Travelletti
D. Ginsbourger
N. Linde
30
3
0
08 Sep 2021
Sequential Bayesian optimal experimental design for structural
  reliability analysis
Sequential Bayesian optimal experimental design for structural reliability analysis
C. Agrell
Kristina Rognlien Dahl
21
21
0
01 Jul 2020
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
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
Kernels over Sets of Finite Sets using RKHS Embeddings, with Application
  to Bayesian (Combinatorial) Optimization
Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization
Poompol Buathong
D. Ginsbourger
Tipaluck Krityakierne
BDL
29
22
0
09 Oct 2019
Knowledge Gradient for Selection with Covariates: Consistency and
  Computation
Knowledge Gradient for Selection with Covariates: Consistency and Computation
Liang Ding
L. Hong
Haihui Shen
Xiaowei Zhang
BDL
13
27
0
12 Jun 2019
The Kalai-Smorodinski solution for many-objective Bayesian optimization
The Kalai-Smorodinski solution for many-objective Bayesian optimization
M. Binois
Victor Picheny
P. Taillandier
A. Habbal
24
17
0
18 Feb 2019
Bayesian Optimization with Expensive Integrands
Bayesian Optimization with Expensive Integrands
Saul Toscano-Palmerin
P. Frazier
18
49
0
23 Mar 2018
Finite-dimensional Gaussian approximation with linear inequality
  constraints
Finite-dimensional Gaussian approximation with linear inequality constraints
A. F. López-Lopera
F. Bachoc
N. Durrande
O. Roustant
17
67
0
20 Oct 2017
Adaptive Design of Experiments for Conservative Estimation of Excursion
  Sets
Adaptive Design of Experiments for Conservative Estimation of Excursion Sets
Dario Azzimonti
D. Ginsbourger
C. Chevalier
Julien Bect
Y. Richet
27
43
0
22 Nov 2016
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