Tiangang Cui

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 32

Top Topics

Monte Carlo Methods

Probability Measure

Inverse Problems

Top Venues

SIAM J. Sci. Comput.

J. Comput. Phys.

SIAM/ASA J. Uncertain. Quantification

Publications

Tiangang Cui, Sergey Dolgov, Robert Scheichl
Deep Importance Sampling Using Tensor Trains with Application to a Priori and a Posteriori Rare Events. SIAM J. Sci. Comput. 46 (1) (2024)
Tiangang Cui, Hans De Sterck, Alexander D. Gilbert, Stanislav Polishchuk, Robert Scheichl
Multilevel Monte Carlo Methods for Stochastic Convection-Diffusion Eigenvalue Problems. J. Sci. Comput. 99 (3) (2024)
Matthew T. C. Li, Tiangang Cui, Fengyi Li, Youssef M. Marzouk, Olivier Zahm
Sharp detection of low-dimensional structure in probability measures via dimensional logarithmic Sobolev inequalities. CoRR (2024)
Benjamin Zanger, Tiangang Cui, Martin Schreiber, Olivier Zahm
Sequential transport maps using SoS density estimation and α-divergences. CoRR (2024)
Tiangang Cui, Friedrich Pillichshammer
Bernstein approximation and beyond: proofs by means of elementary probability theory. CoRR (2023)
Yiran Zhao, Tiangang Cui
Tensor-based Methods for Sequential State and Parameter Estimation in State Space Models. CoRR (2023)
Tiangang Cui, Hans De Sterck, Alexander D. Gilbert, Stanislav Polishchuk, Robert Scheichl
Multilevel Monte Carlo methods for stochastic convection-diffusion eigenvalue problems. CoRR (2023)
Tiangang Cui, Josef Dick, Friedrich Pillichshammer
Quasi-Monte Carlo methods for mixture distributions and approximated distributions via piecewise linear interpolation. CoRR (2023)
Tiangang Cui, Sergey Dolgov, Olivier Zahm
Scalable conditional deep inverse Rosenblatt transports using tensor trains and gradient-based dimension reduction. J. Comput. Phys. 485 (2023)
Tiangang Cui, Sergey Dolgov, Olivier Zahm
Self-reinforced polynomial approximation methods for concentrated probability densities. CoRR (2023)
Olivier Zahm, Tiangang Cui, Kody J. H. Law, Alessio Spantini, Youssef M. Marzouk
Certified dimension reduction in nonlinear Bayesian inverse problems. Math. Comput. 91 (336) (2022)
Tiangang Cui, Sergey Dolgov, Robert Scheichl
Deep importance sampling using tensor-trains with application to a priori and a posteriori rare event estimation. CoRR (2022)
Tiangang Cui, Sergey Dolgov
Deep Composition of Tensor-Trains Using Squared Inverse Rosenblatt Transports. Found. Comput. Math. 22 (6) (2022)
Tiangang Cui, Zhongjian Wang, Zhiwen Zhang
A variational neural network approach for glacier modelling with nonlinear rheology. CoRR (2022)
Tiangang Cui, Xin Tong, Olivier Zahm
Prior normalization for certified likelihood-informed subspace detection of Bayesian inverse problems. CoRR (2022)
Tiangang Cui, Sergey Dolgov, Olivier Zahm
Conditional Deep Inverse Rosenblatt Transports. CoRR (2021)
Lingbin Bian, Tiangang Cui, B. T. Thomas Yeo, Alex Fornito, Adeel Razi, Jonathan Keith
Identification of community structure-based brain states and transitions using functional MRI. NeuroImage 244 (2021)
Johnathan M. Bardsley, Tiangang Cui
Optimization-Based Markov Chain Monte Carlo Methods for Nonlinear Hierarchical Statistical Inverse Problems. SIAM/ASA J. Uncertain. Quantification 9 (1) (2021)
Tiangang Cui, Olivier Zahm
Data-Free Likelihood-Informed Dimension Reduction of Bayesian Inverse Problems. CoRR (2021)
Johnathan M. Bardsley, Tiangang Cui
Optimization-Based MCMC Methods for Nonlinear Hierarchical Statistical Inverse Problems. CoRR (2020)
Johnathan M. Bardsley, Tiangang Cui, Youssef M. Marzouk, Zheng Wang
Scalable Optimization-Based Sampling on Function Space. SIAM J. Sci. Comput. 42 (2) (2020)
Tiangang Cui, Sergey Dolgov
Deep Composition of Tensor Trains using Squared Inverse Rosenblatt Transports. CoRR (2020)
Tiangang Cui, Gianluca Detommaso, Robert Scheichl
Multilevel Dimension-Independent Likelihood-Informed MCMC for Large-Scale Inverse Problems. CoRR (2019)
Gianluca Detommaso, Hanne Hoitzing, Tiangang Cui, Ardavan Alamir
Stein Variational Online Changepoint Detection with Applications to Hawkes Processes and Neural Networks. CoRR (2019)
Gianluca Detommaso, Tiangang Cui, Youssef M. Marzouk, Alessio Spantini, Robert Scheichl
A Stein variational Newton method. NeurIPS (2018)
Siyuan Wu, Tiangang Cui, Tianhai Tian
Mathematical Modelling of Genetic Network for Regulating the Fate Determination of Hematopoietic Stem Cells. BIBM (2018)
Gianluca Detommaso, Tiangang Cui, Youssef M. Marzouk, Robert Scheichl, Alessio Spantini
A Stein variational Newton method. CoRR (2018)
Alessio Spantini, Tiangang Cui, Karen Willcox, Luis Tenorio, Youssef M. Marzouk
Goal-Oriented Optimal Approximations of Bayesian Linear Inverse Problems. SIAM J. Sci. Comput. 39 (5) (2017)
Zheng Wang, Johnathan M. Bardsley, Antti Solonen, Tiangang Cui, Youssef M. Marzouk
Priors: A Randomize-Then-Optimize Approach. SIAM J. Sci. Comput. 39 (5) (2017)
Tiangang Cui, Kody J. H. Law, Youssef M. Marzouk
Dimension-independent likelihood-informed MCMC. J. Comput. Phys. 304 (2016)
Tiangang Cui, Youssef M. Marzouk, Karen Willcox
Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction. J. Comput. Phys. 315 (2016)
Alessio Spantini, Antti Solonen, Tiangang Cui, James Martin, Luis Tenorio, Youssef M. Marzouk
Optimal Low-rank Approximations of Bayesian Linear Inverse Problems. SIAM J. Sci. Comput. 37 (6) (2015)