Search Results for author: Tiangang Cui

Found 9 papers, 3 papers with code

Self-reinforced polynomial approximation methods for concentrated probability densities

no code implementations5 Mar 2023 Tiangang Cui, Sergey Dolgov, Olivier Zahm

We approximate the complicated target density by a composition of self-reinforced KR rearrangements, in which previously constructed KR rearrangements -- based on the same approximation ansatz -- are used to precondition the density approximation problem for building each new KR rearrangement.

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

no code implementations5 Sep 2022 Tiangang Cui, Sergey Dolgov, Robert Scheichl

We approximate the optimal importance distribution in a general importance sampling problem as the pushforward of a reference distribution under a composition of order-preserving transformations, in which each transformation is formed by a squared tensor-train decomposition.

Bayesian Inference

A variational neural network approach for glacier modelling with nonlinear rheology

no code implementations5 Sep 2022 Tiangang Cui, Zhongjian Wang, Zhiwen Zhang

We first formulate the solution of non-Newtonian ice flow model into the minimizer of a variational integral with boundary constraints.

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

1 code implementation8 Jun 2021 Tiangang Cui, Sergey Dolgov, Olivier Zahm

We present a novel offline-online method to mitigate the computational burden of the characterization of posterior random variables in statistical learning.

Bayesian Inference Dimensionality Reduction

Identification of brain states, transitions, and communities using functional MRI

no code implementations26 Jan 2021 Lingbin Bian, Tiangang Cui, B. T. Thomas Yeo, Alex Fornito, Adeel Razi, Jonathan Keith

Brain function relies on a precisely coordinated and dynamic balance between the functional integration and segregation of distinct neural systems.

Time Series Analysis

Deep composition of tensor-trains using squared inverse Rosenblatt transports

no code implementations14 Jul 2020 Tiangang Cui, Sergey Dolgov

The recent surge of transport maps offers a mathematical foundation and new insights for tackling this challenge by coupling intractable random variables with tractable reference random variables.

Optimization-Based MCMC Methods for Nonlinear Hierarchical Statistical Inverse Problems

no code implementations15 Feb 2020 Johnathan Bardsley, Tiangang Cui

In this work, we aim to develop scalable optimization-based Markov chain Monte Carlo (MCMC) methods for solving hierarchical Bayesian inverse problems with nonlinear parameter-to-observable maps and a broader class of hyperparameters.

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

1 code implementation23 Jan 2019 Gianluca Detommaso, Hanne Hoitzing, Tiangang Cui, Ardavan Alamir

Bayesian online changepoint detection (BOCPD) (Adams & MacKay, 2007) offers a rigorous and viable way to identify changepoints in complex systems.

A Stein variational Newton method

1 code implementation NeurIPS 2018 Gianluca Detommaso, Tiangang Cui, Alessio Spantini, Youssef Marzouk, Robert Scheichl

Stein variational gradient descent (SVGD) was recently proposed as a general purpose nonparametric variational inference algorithm [Liu & Wang, NIPS 2016]: it minimizes the Kullback-Leibler divergence between the target distribution and its approximation by implementing a form of functional gradient descent on a reproducing kernel Hilbert space.

Variational Inference

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