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A supermartingale approach to Gaussian process based sequential design of experiments

3 August 2016

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

23 / 23 papers shown

Title
Bayesian Optimization for Non-Convex Two-Stage Stochastic Optimization Problems Jack M. Buckingham Ivo Couckuyt Juergen Branke 83 0 0 30 Aug 2024
Pseudo-Bayesian Optimization Haoxian Chen Henry Lam 112 2 0 15 Oct 2023
Disintegration of Gaussian Measures for Sequential Assimilation of Linear Operator Data Cédric Travelletti D. Ginsbourger 72 6 0 27 Jul 2022
Bayesian Optimization of Function Networks Raul Astudillo P. Frazier 78 37 0 31 Dec 2021
Posterior contraction rates for constrained deep Gaussian processes in density estimation and classication François Bachoc A. Lagnoux 82 4 0 14 Dec 2021
Continuous logistic Gaussian random measure fields for spatial distributional modelling Athénais Gautier D. Ginsbourger 39 0 0 06 Oct 2021
Uncertainty Quantification and Experimental Design for Large-Scale Linear Inverse Problems under Gaussian Process Priors Cédric Travelletti D. Ginsbourger N. Linde 68 4 0 08 Sep 2021
Locally induced Gaussian processes for large-scale simulation experiments D. Cole R. Christianson R. Gramacy 77 21 0 28 Aug 2020
Sequential design of multi-fidelity computer experiments: maximizing the rate of stepwise uncertainty reduction Rémi Stroh Julien Bect S. Demeyer N. Fischer Damien Marquis E. Vázquez 33 13 0 27 Jul 2020
Learning excursion sets of vector-valued Gaussian random fields for autonomous ocean sampling T. Fossum Cédric Travelletti J. Eidsvik D. Ginsbourger K. Rajan 32 18 0 07 Jul 2020
Sequential Bayesian optimal experimental design for structural reliability analysis C. Agrell Kristina Rognlien Dahl 56 21 0 01 Jul 2020
Uncertainty quantification using martingales for misspecified Gaussian processes Willie Neiswanger Aaditya Ramdas UQCV 50 14 0 12 Jun 2020
Additive stacking for disaggregate electricity demand forecasting Christian Capezza B. Palumbo Y. Goude S. Wood Matteo Fasiolo AI4TS 85 7 0 20 May 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 75 93 0 14 Oct 2019
Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization Poompol Buathong D. Ginsbourger Tipaluck Krityakierne BDL 90 22 0 09 Oct 2019
Knowledge Gradient for Selection with Covariates: Consistency and Computation Liang Ding L. Hong Haihui Shen Xiaowei Zhang BDL 100 27 0 12 Jun 2019
Parallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluations Marko Jarvenpaa Michael U. Gutmann Aki Vehtari Pekka Marttinen 118 41 0 03 May 2019
Bayesian quadrature and energy minimization for space-filling design L. Pronzato A. Zhigljavsky 122 9 0 31 Aug 2018
Composite likelihood estimation for a gaussian process under fixed domain asymptotics François Bachoc M. Bevilacqua D. Velandia 51 12 0 24 Jul 2018
Maximum likelihood estimation for Gaussian processes under inequality constraints François Bachoc A. Lagnoux A. F. López-Lopera 87 24 0 10 Apr 2018
Finite-dimensional Gaussian approximation with linear inequality constraints A. F. López-Lopera François Bachoc N. Durrande O. Roustant 138 67 0 20 Oct 2017
Adaptive Design of Experiments for Conservative Estimation of Excursion Sets Dario Azzimonti D. Ginsbourger C. Chevalier Julien Bect Y. Richet 93 44 0 22 Nov 2016
A Bayesian optimization approach to find Nash equilibria Victor Picheny M. Binois A. Habbal 84 35 0 08 Nov 2016