116 citations · 305 across the 40 of their papers we have counts for
40 papers
On Estimating the Gradient of the Expected Information Gain in Bayesian Experimental Design
Ziqiao Ao, Jinglai Li
Bayesian Experimental Design (BED), which aims to find the optimal experimental conditions for Bayesian inference, is usually posed as to optimize the expected information gain (EI…
Deep Unrolling Networks with Recurrent Momentum Acceleration for Nonlinear Inverse Problems
Qingping Zhou, Jiayu Qian, Junqi Tang +1
Combining the strengths of model-based iterative algorithms and data-driven deep learning solutions, deep unrolling networks (DuNets) have become a popular tool to solve inverse im…
Entropy Estimation via Uniformization
Ziqiao Ao, Jinglai Li
Entropy estimation is of practical importance in information theory and statistical science. Many existing entropy estimators suffer from fast growing estimation bias with respect…
Approximate Primal-Dual Fixed-Point based Langevin Algorithms for Non-smooth Convex Potentials
Ziruo Cai, Jinglai Li, Xiaoqun Zhang
The Langevin algorithms are frequently used to sample the posterior distributions in Bayesian inference. In many practical problems, however, the posterior distributions often cons…
Parameter estimation from aggregate observations: A Wasserstein distance based sequential Monte Carlo sampler
Chen Cheng, Linjie Wen, Jinglai Li
In this work we study systems consisting of a group of moving particles. In such systems, often some important parameters are unknown and have to be estimated from observed data. S…
VI-DGP: A variational inference method with deep generative prior for solving high-dimensional inverse problems
Yingzhi Xia, Qifeng Liao, Jinglai Li
Solving high-dimensional Bayesian inverse problems (BIPs) with the variational inference (VI) method is promising but still challenging. The main difficulties arise from two aspect…