Fetching the paper…
Reading the bibliography…
The curse of dimensionality is a longstanding challenge in Bayesian inference in high dimensions.
Hessian-based model reduction for large-scale systems with initial condition inputs
O. Bashir, K. Willcox, O. Ghattas, B. van Bloemen Waanders, and J. Hill · 2008
Earlier work this paper cites.
Riemann manifold Langevin and Hamiltonian Monte Carlo methods
Mark Girolami and Ben Calderhead · 2011
Earlier work this paper cites.
Analysis of the Hessian for inverse scattering problems: I. Inverse shape scattering of acoustic waves
T. Bui-Thanh and O. Ghattas · 2012
Earlier work this paper cites.
A stochastic Newton MCMC method for large-scale statistical inverse problems with application to seismic inversion
J. Martin, L.C. Wilcox, C. Burstedde, and O. Ghattas · 2012
Earlier work this paper cites.
Sparse deterministic approximation of Bayesian inverse problems
Ch. Schwab and A.M. Stuart · 2012
Earlier work this paper cites.
A computational framework for infinite-dimensional bayesian inverse problems part I: The linearized case, with application to global seismic inversion
T. Bui-Thanh, O. Ghattas, J. Martin, and G. Stadler · 2013
Earlier work this paper cites.
Sparse, adaptive Smolyak quadratures for Bayesian inverse problems
Claudia Schillings and Christoph Schwab · 2013
Earlier work this paper cites.
A computational framework for infinite-dimensional Bayesian inverse problems, part ii: Stochastic Newton MCMC with application to ice sheet flow inverse problems
N. Petra, J. Martin, G. Stadler, and O. Ghattas · 2014
Earlier work this paper cites.
Sparse-grid, reduced-basis Bayesian inversion
Peng Chen and Christoph Schwab · 2015
Earlier work this paper cites.
Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the Antarctic ice sheet
Tobin Isaac, Noemi Petra, Georg Stadler, and Omar Ghattas · 2015
Earlier work this paper cites.
On the decreasing power of kernel and distance based nonparametric hypothesis tests in high dimensions
Aaditya Ramdas, Sashank Jakkam Reddi, Barnabás Póczos, Aarti Singh, and Larry Wasserman · 2015
Earlier work this paper cites.
Optimal low-rank approximations of Bayesian linear inverse problems
A. Spantini, A. Solonen, T. Cui, J. Martin, L. Tenorio, and Y. Marzouk · 2015
Earlier work this paper cites.
Sparse-grid, reduced-basis Bayesian inversion: Nonaffine-parametric nonlinear equations
Peng Chen and Christoph Schwab · 2016
Cited alongside, same era.
Accelerating Markov chain Monte Carlo with active subspaces
Paul G Constantine, Carson Kent, and Tan Bui-Thanh · 2016
Cited alongside, same era.
Dimension-independent likelihood-informed MCMC
Tiangang Cui, Kody JH Law, and Youssef M Marzouk · 2016
Cited alongside, same era.
Stein variational gradient descent: A general purpose Bayesian inference algorithm
Qiang Liu and Dilin Wang · 2016
Cited alongside, same era.
Sampling via measure transport: An introduction
Youssef Marzouk, Tarek Moselhy, Matthew Parno, and Alessio Spantini · 2016
Cited alongside, same era.
Randomized algorithms for generalized Hermitian eigenvalue problems with application to computing Karhunen–Loève expansion
A stein variational Newton method
Gianluca Detommaso, Tiangang Cui, Youssef Marzouk, Alessio Spantini, and Robert Scheichl · 2018
Later among the works it cites.
Riemannian Stein variational gradient descent for Bayesian inference
Chang Liu and Jun Zhu · 2018
Later among the works it cites.
Stein variational message passing for continuous graphical models
Dilin Wang, Zhe Zeng, and Qiang Liu · 2018
Later among the works it cites.
Certified dimension reduction in nonlinear Bayesian inverse problems
Olivier Zahm, Tiangang Cui, Kody Law, Alessio Spantini, and Youssef Marzouk · 2018
Later among the works it cites.
Message passing Stein variational gradient descent
Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen, and Bo Zhang · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A.K. Saibaba, J. Lee, and P.K. Kitanidis · 2016
Cited alongside, same era.
Scaling limits in computational Bayesian inversion
Claudia Schillings and Christoph Schwab · 2016
Cited alongside, same era.
Geometric MCMC for infinite-dimensional inverse problems
Alexandros Beskos, Mark Girolami, Shiwei Lan, Patrick E. Farrell, and Andrew M. Stuart · 2017
Cited alongside, same era.
Variational inference: A review for statisticians
David M Blei, Alp Kucukelbir, and Jon D McAuliffe · 2017
Cited alongside, same era.
Hessian-based adaptive sparse quadrature for infinite-dimensional Bayesian inverse problems
Peng Chen, Umberto Villa, and Omar Ghattas · 2017
Cited alongside, same era.
Wilson Ye Chen, Lester Mackey, Jackson Gorham, François-Xavier Briol, and Chris J Oates · 2018
Cited alongside, same era.
Daniele Bigoni, Olivier Zahm, Alessio Spantini, and Youssef Marzouk · 2019
Later among the works it cites.
Hessian-based sampling for high-dimensional model reduction
Peng Chen and Omar Ghattas · 2019
Later among the works it cites.
Taylor approximation and variance reduction for PDE-constrained optimal control problems under uncertainty
Peng Chen, Umberto Villa, and Omar Ghattas · 2019
Later among the works it cites.
Projected Stein variational Newton: A fast and scalable Bayesian inference method in high dimensions
Peng Chen, Keyi Wu, Joshua Chen, Tom O’Leary-Roseberry, and Omar Ghattas · 2019
Later among the works it cites.
HINT: Hierarchical invertible neural transport for general and sequential Bayesian inference
Gianluca Detommaso, Jakob Kruse, Lynton Ardizzone, Carsten Rother, Ullrich Köthe, and Robert Scheichl · 2019
Later among the works it cites.
Information Newton’s flow: second-order optimization method in probability space
Yifei Wang and Wuchen Li · 2020
Closest in time.