Search Results for author:
Found 9 papers, 3 papers with code
Self-reinforced polynomial approximation methods for concentrated probability densities
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
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.
A variational neural network approach for glacier modelling with nonlinear rheology
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
We present a novel offline-online method to mitigate the computational burden of the characterization of posterior random variables in statistical learning.
Identification of brain states, transitions, and communities using functional MRI
Brain function relies on a precisely coordinated and dynamic balance between the functional integration and segregation of distinct neural systems.
Deep composition of tensor-trains using squared inverse Rosenblatt transports
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
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
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
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.
