4 papers
A New Convergence Analysis of Plug-and-Play Proximal Gradient Descent Under Prior Mismatch
Guixian Xu, Jinglai Li, Junqi Tang
In this work, we provide a new convergence theory for plug-and-play proximal gradient descent (PnP-PGD) under prior mismatch where the denoiser is trained on a different data distr…
The Practicality of Normalizing Flow Test-Time Training in Bayesian Inference for Agent-Based Models
Junyao Zhang, Jinglai Li, Junqi Tang
Agent-Based Models (ABMs) are gaining great popularity in economics and social science because of their strong flexibility to describe the realistic and heterogeneous decisions and…
Group Symmetry Enables Faster Optimization in Inverse Problems
Junqi Tang, Guixian Xu
We prove for the first time that, if a linear inverse problem exhibits a group symmetry structure, gradient-based optimizers can be designed to exploit this structure for faster co…
Equivariant Test-Time Training with Operator Sketching for Imaging Inverse Problems
Guixian Xu, Jinglai Li, Junqi Tang
Equivariant Imaging (EI) regularization has become the de-facto technique for unsupervised training of deep imaging networks, without any need of ground-truth data. Observing that…