9 papers
Domain Adaptation of Mismatched Proximal Denoiser for Plug-and-Play Image Reconstruction
Guixian Xu, Jinglai Li, Junqi Tang
Plug-and-play proximal gradient descent (PnP-PGD) enables flexible image reconstruction by using denoisers as implicit priors. In practice, these denoisers are often deployed outsi…
Exponential-Family Tensor Completion via Nonconvex Dual Total-Variation Regularization
Wenfei Cao, Yang Chen, Qibin Zhao +2
With the emergence of various tensor data, tensor completion from partial measurements has attracted widespread attention in data science and signal processing. Total Variation (TV…
Segmenting Human-LLM Co-authored Text via Change Point Detection
Mengchu Li, Jin Zhu, Jinglai Li +1
The rise of large language models (LLMs) has created an urgent need to distinguish between human-written and LLM-generated text to ensure authenticity and societal trust. Existing…
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…
Adapting Neural Posterior Estimators for Simulation-Based Inference in Agent-Based Models
Junyao Zhang, Jinglai Li, Junqi Tang
Agent-based models (ABMs) are widely used in economics and social sciences. They represent heterogeneous personal behaviors and their interactions by using simulations, whereas the…
Fast Equivariant Imaging: Accelerating Unsupervised Learning and Model Adaptation via Inexact Splitting
Guixian Xu, Jinglai Li, Junqi Tang
In this work, we propose Fast Equivariant Imaging (FEI), a novel unsupervised learning framework to rapidly and efficiently train deep imaging networks without ground-truth data. F…
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…
NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problems
Ziruo Cai, Junqi Tang, Subhadip Mukherjee +3
Bayesian methods for solving inverse problems are a powerful alternative to classical methods since the Bayesian approach offers the ability to quantify the uncertainty in the solu…
Fast Gradient Methods for Data-Consistent Local Super-Resolution of Medical Images
Junqi Tang, Guixian Xu, Jinglai Li
In this work, we propose a new paradigm of iterative model-based reconstruction algorithms for providing real-time solution for zooming-in and refining a region of interest in medi…