In preparation / Under review:

  1. Derek Driggs*, Junqi Tang*, Jingwei Liang, Mike Davies, Carola-Bibiane Schönlieb. SPRING: A Fast Stochastic Proximal Alternating Method for Non-smooth Non-convex Optimization.  2021 [Preprint][Code]
  2. Junqi Tang, Mike Davies.  A Fast Stochastic Plug-and-Play ADMM for Imaging Inverse Problems. 2021 [Preprint]
  3. Bin Qian, Zhenyu Wen, Junqi Tang, Ye Yuan, Albert Zomaya, Rajiv Ranjan. OsmoticGate: Adaptive Edge-based Real-time Video Analytics for the Internet of Things.  2021 [Preprint]


  1. Julian Tachella, Junqi Tang, Mike Davies. The Neural Tangent Link Between CNN Denoisers and Non-local Filters.  Computer Vision and Pattern Recognition (CVPR), 2021 (to appear, Oral) [link][Code]
  2. Junqi Tang, Karen Egiazarian,  Mohammad Golbabaee, Mike Davies. The Practicality of Stochastic Optimization in Imaging Inverse Problems. IEEE Transactions on Computational Imaging, 2020 [Download PDF]
  3. Junqi Tang, Karen Egiazarian,  Mike Davies. The Limitation and Practical Acceleration of Stochastic Gradient Algorithms in Inverse Problems. International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019 [Download PDF] [slides]
  4. Junqi Tang, Mohammad Golbabaee, Francis Bach,  Mike Davies. Rest-Katyusha: Exploiting the Solution’s Structure via Scheduled Restart Schemes. Advances in Neural Information Processing Systems (NeurIPS), 2018.[Download PDF] [Poster]
  5. Junqi Tang, Mohammad Golbabaee, Mike Davies. Exploiting the Structure via Sketched Gradient Algorithms. IEEE conference on Signal and Information Processing (GlobalSIP), 2017.[Download PDF]
  6. Junqi Tang, Mohammad Golbabaee, Mike Davies. Gradient Projection Iterative Sketch for Large-Scale Constrained Least-Squares. International Conference on Machine Learning (ICML), 2017.[Download PDF] [slides]

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