Tiangang Cui - RankMe

40 papers A* 1Journal 38Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A tensor-train reduced basis solver for parameterized partial differential equations on Cartesian grids. J. Comput. Appl. Math. Nicholas Mueller, Yiran Zhao, Santiago Badia, Tiangang Cui
2025	J	`jnl`	Quasi-Monte Carlo methods for mixture distributions and approximated distributions via piecewise linear interpolation. Adv. Comput. Math. Tiangang Cui, Josef Dick, Friedrich Pillichshammer
2025	J	`jnl`	Second order interlaced polynomial lattice rules for integration over ℝ CoRR Tiangang Cui, Josef Dick, Friedrich Pillichshammer
2025	J	`jnl`	Subspace accelerated measure transport methods for fast and scalable sequential experimental design, with application to photoacoustic imaging. CoRR Tiangang Cui, Karina Koval, Roland Herzog, Robert Scheichl
2024	J	`jnl`	A tensor-train reduced basis solver for parameterized partial differential equations. CoRR Nicholas Mueller, Yiran Zhao, Santiago Badia, Tiangang Cui
2024	J	`jnl`	Deep Importance Sampling Using Tensor Trains with Application to a Priori and a Posteriori Rare Events. SIAM J. Sci. Comput. Tiangang Cui, Sergey Dolgov, Robert Scheichl
2024	J	`jnl`	Low-rank Bayesian matrix completion via geodesic Hamiltonian Monte Carlo on Stiefel manifolds. CoRR Tiangang Cui, Alex A. Gorodetsky
2024	J	`jnl`	Multilevel Monte Carlo Methods for Stochastic Convection-Diffusion Eigenvalue Problems. J. Sci. Comput. Tiangang Cui, Hans De Sterck, Alexander D. Gilbert, Stanislav Polishchuk, Robert Scheichl
2024	J	`jnl`	Sequential transport maps using SoS density estimation and α-divergences. CoRR Benjamin Zanger, Tiangang Cui, Martin Schreiber, Olivier Zahm
2024	J	`jnl`	Sharp detection of low-dimensional structure in probability measures via dimensional logarithmic Sobolev inequalities. CoRR Matthew T. C. Li, Tiangang Cui, Fengyi Li, Youssef M. Marzouk, Olivier Zahm
2024	J	`jnl`	Tensor-train methods for sequential state and parameter learning in state-space models. J. Mach. Learn. Res. Yiran Zhao, Tiangang Cui
2024	J	`jnl`	\ell CoRR Tiangang Cui, Shuigen Liu, Xin Tong
2023	J	`jnl`	Bernstein approximation and beyond: proofs by means of elementary probability theory. CoRR Tiangang Cui, Friedrich Pillichshammer
2023	J	`jnl`	Multilevel Monte Carlo methods for stochastic convection-diffusion eigenvalue problems. CoRR Tiangang Cui, Hans De Sterck, Alexander D. Gilbert, Stanislav Polishchuk, Robert Scheichl
2023	J	`jnl`	Quasi-Monte Carlo methods for mixture distributions and approximated distributions via piecewise linear interpolation. CoRR Tiangang Cui, Josef Dick, Friedrich Pillichshammer
2023	J	`jnl`	Scalable conditional deep inverse Rosenblatt transports using tensor trains and gradient-based dimension reduction. J. Comput. Phys. Tiangang Cui, Sergey Dolgov, Olivier Zahm
2023	J	`jnl`	Self-reinforced polynomial approximation methods for concentrated probability densities. CoRR Tiangang Cui, Sergey Dolgov, Olivier Zahm
2023	J	`jnl`	Tensor-based Methods for Sequential State and Parameter Estimation in State Space Models. CoRR Yiran Zhao, Tiangang Cui
2022	J	`jnl`	A variational neural network approach for glacier modelling with nonlinear rheology. CoRR Tiangang Cui, Zhongjian Wang, Zhiwen Zhang
2022	J	`jnl`	Certified dimension reduction in nonlinear Bayesian inverse problems. Math. Comput. Olivier Zahm, Tiangang Cui, Kody J. H. Law, Alessio Spantini, Youssef M. Marzouk
2022	J	`jnl`	Deep Composition of Tensor-Trains Using Squared Inverse Rosenblatt Transports. Found. Comput. Math. Tiangang Cui, Sergey Dolgov
2022	J	`jnl`	Deep importance sampling using tensor-trains with application to a priori and a posteriori rare event estimation. CoRR Tiangang Cui, Sergey Dolgov, Robert Scheichl
2022	J	`jnl`	Prior normalization for certified likelihood-informed subspace detection of Bayesian inverse problems. CoRR Tiangang Cui, Xin Tong, Olivier Zahm
2021	J	`jnl`	Conditional Deep Inverse Rosenblatt Transports. CoRR Tiangang Cui, Sergey Dolgov, Olivier Zahm
2021	J	`jnl`	Data-Free Likelihood-Informed Dimension Reduction of Bayesian Inverse Problems. CoRR Tiangang Cui, Olivier Zahm
2021	J	`jnl`	Identification of community structure-based brain states and transitions using functional MRI. NeuroImage Lingbin Bian, Tiangang Cui, B. T. Thomas Yeo, Alex Fornito, Adeel Razi, Jonathan Keith
2021	J	`jnl`	Optimization-Based Markov Chain Monte Carlo Methods for Nonlinear Hierarchical Statistical Inverse Problems. SIAM/ASA J. Uncertain. Quantification Johnathan M. Bardsley, Tiangang Cui
2020	J	`jnl`	Deep Composition of Tensor Trains using Squared Inverse Rosenblatt Transports. CoRR Tiangang Cui, Sergey Dolgov
2020	J	`jnl`	Optimization-Based MCMC Methods for Nonlinear Hierarchical Statistical Inverse Problems. CoRR Johnathan M. Bardsley, Tiangang Cui
2020	J	`jnl`	Scalable Optimization-Based Sampling on Function Space. SIAM J. Sci. Comput. Johnathan M. Bardsley, Tiangang Cui, Youssef M. Marzouk, Zheng Wang
2019	J	`jnl`	Multilevel Dimension-Independent Likelihood-Informed MCMC for Large-Scale Inverse Problems. CoRR Tiangang Cui, Gianluca Detommaso, Robert Scheichl
2019	J	`jnl`	Stein Variational Online Changepoint Detection with Applications to Hawkes Processes and Neural Networks. CoRR Gianluca Detommaso, Hanne Hoitzing, Tiangang Cui, Ardavan Alamir
2018	A*	`conf`	A Stein variational Newton method. NeurIPS Gianluca Detommaso, Tiangang Cui, Youssef M. Marzouk, Alessio Spantini, Robert Scheichl
2018	J	`jnl`	A Stein variational Newton method. CoRR Gianluca Detommaso, Tiangang Cui, Youssef M. Marzouk, Robert Scheichl, Alessio Spantini
2018	—	`conf`	Mathematical Modelling of Genetic Network for Regulating the Fate Determination of Hematopoietic Stem Cells. BIBM Siyuan Wu, Tiangang Cui, Tianhai Tian
2017	J	`jnl`	Bayesian Inverse Problems with l SIAM J. Sci. Comput. Zheng Wang, Johnathan M. Bardsley, Antti Solonen, Tiangang Cui, Youssef M. Marzouk
2017	J	`jnl`	Goal-Oriented Optimal Approximations of Bayesian Linear Inverse Problems. SIAM J. Sci. Comput. Alessio Spantini, Tiangang Cui, Karen Willcox, Luis Tenorio, Youssef M. Marzouk
2016	J	`jnl`	Dimension-independent likelihood-informed MCMC. J. Comput. Phys. Tiangang Cui, Kody J. H. Law, Youssef M. Marzouk
2016	J	`jnl`	Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction. J. Comput. Phys. Tiangang Cui, Youssef M. Marzouk, Karen Willcox
2015	J	`jnl`	Optimal Low-rank Approximations of Bayesian Linear Inverse Problems. SIAM J. Sci. Comput. Alessio Spantini, Antti Solonen, Tiangang Cui, James Martin, Luis Tenorio, Youssef M. Marzouk