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2020 – today
- 2024
[j14]Yiran Zhao, Tiangang Cui:
Tensor-train methods for sequential state and parameter learning in state-space models. J. Mach. Learn. Res. 25: 244:1-244:51 (2024)- [j13]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): 77 (2024) - [j12]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): 1- (2024) - [i17]Benjamin Zanger, Tiangang Cui, Martin Schreiber, Olivier Zahm:
Sequential transport maps using SoS density estimation and α-divergences. CoRR abs/2402.17943 (2024) - [i16]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 abs/2406.13036 (2024) - 2023
- [j11]Tiangang Cui, Sergey Dolgov, Olivier Zahm:
Scalable conditional deep inverse Rosenblatt transports using tensor trains and gradient-based dimension reduction. J. Comput. Phys. 485: 112103 (2023) - [i15]Yiran Zhao, Tiangang Cui:
Tensor-based Methods for Sequential State and Parameter Estimation in State Space Models. CoRR abs/2301.09891 (2023) - [i14]Tiangang Cui, Sergey Dolgov, Olivier Zahm:
Self-reinforced polynomial approximation methods for concentrated probability densities. CoRR abs/2303.02554 (2023) - [i13]Tiangang Cui, Hans De Sterck, Alexander D. Gilbert, Stanislav Polishchuk, Robert Scheichl:
Multilevel Monte Carlo methods for stochastic convection-diffusion eigenvalue problems. CoRR abs/2303.03673 (2023) - [i12]Tiangang Cui, Josef Dick, Friedrich Pillichshammer:
Quasi-Monte Carlo methods for mixture distributions and approximated distributions via piecewise linear interpolation. CoRR abs/2304.14786 (2023) - [i11]Tiangang Cui, Friedrich Pillichshammer:
Bernstein approximation and beyond: proofs by means of elementary probability theory. CoRR abs/2307.11533 (2023) - 2022
- [j10]Tiangang Cui, Sergey Dolgov:
Deep Composition of Tensor-Trains Using Squared Inverse Rosenblatt Transports. Found. Comput. Math. 22(6): 1863-1922 (2022) - [j9]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): 1789-1835 (2022) - [i10]Tiangang Cui, Xin Tong, Olivier Zahm:
Prior normalization for certified likelihood-informed subspace detection of Bayesian inverse problems. CoRR abs/2202.00074 (2022) - [i9]Tiangang Cui, Sergey Dolgov, Robert Scheichl:
Deep importance sampling using tensor-trains with application to a priori and a posteriori rare event estimation. CoRR abs/2209.01941 (2022) - [i8]Tiangang Cui, Zhongjian Wang, Zhiwen Zhang:
A variational neural network approach for glacier modelling with nonlinear rheology. CoRR abs/2209.02088 (2022) - 2021
- [j8]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): 29-64 (2021) - [j7]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: 118635 (2021) - [i7]Tiangang Cui, Olivier Zahm:
Data-Free Likelihood-Informed Dimension Reduction of Bayesian Inverse Problems. CoRR abs/2102.13245 (2021) - [i6]Tiangang Cui, Sergey Dolgov, Olivier Zahm:
Conditional Deep Inverse Rosenblatt Transports. CoRR abs/2106.04170 (2021) - 2020
- [j6]Johnathan M. Bardsley, Tiangang Cui, Youssef M. Marzouk, Zheng Wang:
Scalable Optimization-Based Sampling on Function Space. SIAM J. Sci. Comput. 42(2): A1317-A1347 (2020) - [i5]Johnathan M. Bardsley, Tiangang Cui:
Optimization-Based MCMC Methods for Nonlinear Hierarchical Statistical Inverse Problems. CoRR abs/2002.06358 (2020) - [i4]Tiangang Cui, Sergey Dolgov:
Deep Composition of Tensor Trains using Squared Inverse Rosenblatt Transports. CoRR abs/2007.06968 (2020)
2010 – 2019
- 2019
- [i3]Gianluca Detommaso, Hanne Hoitzing, Tiangang Cui, Ardavan Alamir:
Stein Variational Online Changepoint Detection with Applications to Hawkes Processes and Neural Networks. CoRR abs/1901.07987 (2019) - [i2]Tiangang Cui, Gianluca Detommaso, Robert Scheichl:
Multilevel Dimension-Independent Likelihood-Informed MCMC for Large-Scale Inverse Problems. CoRR abs/1910.12431 (2019) - 2018
- [c2]Siyuan Wu, Tiangang Cui, Tianhai Tian:
Mathematical Modelling of Genetic Network for Regulating the Fate Determination of Hematopoietic Stem Cells. BIBM 2018: 2167-2173 - [c1]Gianluca Detommaso, Tiangang Cui, Youssef M. Marzouk, Alessio Spantini, Robert Scheichl:
A Stein variational Newton method. NeurIPS 2018: 9187-9197 - [i1]Gianluca Detommaso, Tiangang Cui, Youssef M. Marzouk, Robert Scheichl, Alessio Spantini:
A Stein variational Newton method. CoRR abs/1806.03085 (2018) - 2017
- [j5]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) - [j4]Zheng Wang, Johnathan M. Bardsley, Antti Solonen, Tiangang Cui, Youssef M. Marzouk:
Bayesian Inverse Problems with l1 Priors: A Randomize-Then-Optimize Approach. SIAM J. Sci. Comput. 39(5) (2017) - 2016
- [j3]Tiangang Cui, Kody J. H. Law, Youssef M. Marzouk:
Dimension-independent likelihood-informed MCMC. J. Comput. Phys. 304: 109-137 (2016) - [j2]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: 363-387 (2016) - 2015
- [j1]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)
Coauthor Index
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last updated on 2024-09-18 01:08 CEST by the dblp team
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