Tiangang Cui

According to our database, Tiangang Cui authored at least 28 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Deep Importance Sampling Using Tensor Trains with Application to a Priori and a Posteriori Rare Events.

Tiangang Cui, Sergey Dolgov, Robert Scheichl

SIAM J. Sci. Comput., February, 2024

Sequential transport maps using SoS density estimation and α-divergences.

Benjamin Zanger, Tiangang Cui, Martin Schreiber, Olivier Zahm

CoRR, 2024

2023

Scalable conditional deep inverse Rosenblatt transports using tensor trains and gradient-based dimension reduction.

Tiangang Cui, Sergey Dolgov, Olivier Zahm

J. Comput. Phys., 2023

Bernstein approximation and beyond: proofs by means of elementary probability theory.

Tiangang Cui, Friedrich Pillichshammer

CoRR, 2023

Quasi-Monte Carlo methods for mixture distributions and approximated distributions via piecewise linear interpolation.

Tiangang Cui, Josef Dick, Friedrich Pillichshammer

CoRR, 2023

Multilevel Monte Carlo methods for stochastic convection-diffusion eigenvalue problems.

Tiangang Cui, Hans De Sterck, Alexander D. Gilbert, Stanislav Polishchuk, Robert Scheichl

CoRR, 2023

Self-reinforced polynomial approximation methods for concentrated probability densities.

Tiangang Cui, Sergey Dolgov, Olivier Zahm

CoRR, 2023

Tensor-based Methods for Sequential State and Parameter Estimation in State Space Models.

Yiran Zhao, Tiangang Cui

CoRR, 2023

2022

Certified dimension reduction in nonlinear Bayesian inverse problems.

Olivier Zahm, Tiangang Cui, Kody J. H. Law, Alessio Spantini, Youssef M. Marzouk

Math. Comput., 2022

Deep Composition of Tensor-Trains Using Squared Inverse Rosenblatt Transports.

Tiangang Cui, Sergey Dolgov

Found. Comput. Math., 2022

A variational neural network approach for glacier modelling with nonlinear rheology.

Tiangang Cui, Zhongjian Wang, Zhiwen Zhang

CoRR, 2022

Deep importance sampling using tensor-trains with application to a priori and a posteriori rare event estimation.

Tiangang Cui, Sergey Dolgov, Robert Scheichl

CoRR, 2022

Prior normalization for certified likelihood-informed subspace detection of Bayesian inverse problems.

Tiangang Cui, Xin Tong, Olivier Zahm

CoRR, 2022

2021

Identification of community structure-based brain states and transitions using functional MRI.

Lingbin Bian, Tiangang Cui, B. T. Thomas Yeo, Alex Fornito, Adeel Razi, Jonathan Keith

NeuroImage, 2021

Optimization-Based Markov Chain Monte Carlo Methods for Nonlinear Hierarchical Statistical Inverse Problems.

Johnathan M. Bardsley, Tiangang Cui

SIAM/ASA J. Uncertain. Quantification, 2021

Conditional Deep Inverse Rosenblatt Transports.

Tiangang Cui, Sergey Dolgov, Olivier Zahm

CoRR, 2021

Data-Free Likelihood-Informed Dimension Reduction of Bayesian Inverse Problems.

Tiangang Cui, Olivier Zahm

CoRR, 2021

2020

Scalable Optimization-Based Sampling on Function Space.

Johnathan M. Bardsley, Tiangang Cui, Youssef M. Marzouk, Zheng Wang

SIAM J. Sci. Comput., 2020

Optimization-Based MCMC Methods for Nonlinear Hierarchical Statistical Inverse Problems.

Johnathan M. Bardsley, Tiangang Cui

CoRR, 2020

2019

Multilevel Dimension-Independent Likelihood-Informed MCMC for Large-Scale Inverse Problems.

Tiangang Cui, Gianluca Detommaso, Robert Scheichl

CoRR, 2019

Stein Variational Online Changepoint Detection with Applications to Hawkes Processes and Neural Networks.

Gianluca Detommaso, Hanne Hoitzing, Tiangang Cui, Ardavan Alamir

CoRR, 2019

2018

A Stein variational Newton method.

Gianluca Detommaso, Tiangang Cui, Youssef M. Marzouk, Alessio Spantini, Robert Scheichl

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Mathematical Modelling of Genetic Network for Regulating the Fate Determination of Hematopoietic Stem Cells.

Siyuan Wu, Tiangang Cui, Tianhai Tian

Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2018

2017

Bayesian Inverse Problems with l₁ Priors: A Randomize-Then-Optimize Approach.

Zheng Wang, Johnathan M. Bardsley, Antti Solonen, Tiangang Cui, Youssef M. Marzouk

SIAM J. Sci. Comput., 2017

Goal-Oriented Optimal Approximations of Bayesian Linear Inverse Problems.

Alessio Spantini, Tiangang Cui, Karen Willcox, Luis Tenorio, Youssef M. Marzouk

SIAM J. Sci. Comput., 2017

2016

Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction.

Tiangang Cui, Youssef M. Marzouk, Karen Willcox

J. Comput. Phys., 2016

Dimension-independent likelihood-informed MCMC.

Tiangang Cui, Kody J. H. Law, Youssef M. Marzouk

J. Comput. Phys., 2016

2015

Optimal Low-rank Approximations of Bayesian Linear Inverse Problems.

Alessio Spantini, Antti Solonen, Tiangang Cui, James Martin, Luis Tenorio, Youssef M. Marzouk

SIAM J. Sci. Comput., 2015