A Fast Stochastic Plug-and-Play ADMM for Imaging Inverse problems

• Junqi Tang, Mike Davies. 2020
• [Preprint]

CNN Denoisers as Non-Local Filters: The Neural Tangent Denoiser

• Julian Tachella, Junqi Tang, Mike Davies, 2020
• [Preprint]

SPRING: A Fast Stochastic Proximal Alternating Method for Non-smooth Non-convex Optimization

• Derek Driggs*, Junqi Tang*, Jingwei Liang, Mike Davies, Carola-Bibiane Schönlieb, 2020
• [Preprint]

The Practicality of Stochastic Optimization in Imaging Inverse Problems

• Junqi Tang, Karen Egiazarian,  Mohammad Golbabaee, Mike Davies
• IEEE Transactions on Computational Imaging, 2020
• [Download PDF]

The Limitation and Practical Acceleration of Stochastic Gradient Algorithms in Inverse Problems

• Junqi Tang, Karen Egiazarian,  Mike Davies.
• International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019
• [Download PDF]

Structure-Adaptive Accelerated Coordinate Descent

• Junqi Tang, Mohammad Golbabaee, Francis Bach,  Mike Davies.
• Submitted, 2018
• [Download PDF]

Rest-Katyusha: Exploiting the Solution’s Structure via Scheduled Restart Schemes

• Junqi Tang, Mohammad Golbabaee, Francis Bach,  Mike Davies.
• Advances in Neural Information Processing Systems, 2018.
• [Download PDF] [Poster]

Gradient Projection Iterative Sketch for Large-Scale Constrained Least-Squares

• Junqi Tang, Mohammad Golbabaee, Mike Davies.
•  International Conference on Machine Learning (ICML), 2017.
• [Download PDF] [Supplemental material] [bibtex] [slides]

Exploiting the Structure via Sketched Gradient Algorithms

• Junqi Tang, Mohammad Golbabaee, Mike Davies.
• IEEE conference on Signal and Information Processing (GlobalSIP),  2017.
• [Download PDF] [bibtex]