Gradient-based data and parameter dimension reduction for Bayesian
models: an information theoretic perspective

Gradient-based data and parameter dimension reduction for Bayesian models: an information theoretic perspective

18 July 2022

Ricardo Baptista

Youssef Marzouk

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Papers citing "Gradient-based data and parameter dimension reduction for Bayesian models: an information theoretic perspective"

18 / 18 papers shown

Title
Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity Analysis Qiao Chen Elise Arnaud Ricardo Baptista O. Zahm 89 1 0 19 Jun 2024
Learning non-Gaussian graphical models via Hessian scores and triangular transport Ricardo Baptista Youssef Marzouk Rebecca E. Morrison O. Zahm 71 23 0 08 Jan 2021
A unified performance analysis of likelihood-informed subspace methods Tiangang Cui X. Tong 64 26 0 07 Jan 2021
Optimal dimension dependence of the Metropolis-Adjusted Langevin Algorithm Sinho Chewi Chen Lu Kwangjun Ahn Xiang Cheng Thibaut Le Gouic Philippe Rigollet 67 66 0 23 Dec 2020
BayesFlow: Learning complex stochastic models with invertible neural networks Stefan T. Radev U. Mertens A. Voss Lynton Ardizzone Ullrich Kothe BDL 292 197 0 13 Mar 2020
Projected Stein Variational Gradient Descent Peng Chen Omar Ghattas BDL 83 70 0 09 Feb 2020
Subspace Inference for Bayesian Deep Learning Pavel Izmailov Wesley J. Maddox Polina Kirichenko T. Garipov Dmitry Vetrov A. Wilson UQCV BDL 78 144 0 17 Jul 2019
Coupling techniques for nonlinear ensemble filtering Alessio Spantini Ricardo Baptista Youssef Marzouk 90 76 0 30 Jun 2019
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations Brian L. Trippe Jonathan H. Huggins Raj Agrawal Tamara Broderick BDL 61 9 0 17 May 2019
A Stein variational Newton method Gianluca Detommaso Tiangang Cui Alessio Spantini Youssef Marzouk Robert Scheichl 128 117 0 08 Jun 2018
Likelihood-free inference with emulator networks Jan-Matthis Lueckmann Giacomo Bassetto Theofanis Karaletsos Jakob H. Macke 165 128 0 23 May 2018
Automated Scalable Bayesian Inference via Hilbert Coresets Trevor Campbell Tamara Broderick 78 127 0 13 Oct 2017
Variational Inference: A Review for Statisticians David M. Blei A. Kucukelbir Jon D. McAuliffe BDL 305 4,812 0 04 Jan 2016
Variational Inference with Normalizing Flows Danilo Jimenez Rezende S. Mohamed DRL BDL 322 4,197 0 21 May 2015
Dimension-independent likelihood-informed MCMC Tiangang Cui K. Law Youssef M. Marzouk 75 200 0 13 Nov 2014
Optimal low-rank approximations of Bayesian linear inverse problems Alessio Spantini A. Solonen Tiangang Cui James Martin L. Tenorio Youssef Marzouk 99 130 0 13 Jul 2014
Likelihood-informed dimension reduction for nonlinear inverse problems Tiangang Cui James Martin Youssef M. Marzouk A. Solonen Alessio Spantini 85 159 0 19 Mar 2014
The pseudo-marginal approach for efficient Monte Carlo computations Christophe Andrieu Gareth O. Roberts 189 895 0 31 Mar 2009