The Non-uniform Fast Fourier Transform in Computed Tomography. - Junqi Tang (CoRR, 2016)

Exploiting the structure via sketched gradient algorithms. - Junqi Tang; Mohammad Golbabaee; Mike E. Davies 001 (GlobalSIP, 2017)

Gradient Projection Iterative Sketch for Large-Scale Constrained Least-Squares. - Junqi Tang; Mohammad Golbabaee; Mike E. Davies 001 (ICML, 2017)

Rest-Katyusha: Exploiting the Solution's Structure via Scheduled Restart Schemes. - Junqi Tang; Mohammad Golbabaee; Francis R. Bach; Mike E. Davies 001 (NeurIPS, 2018)

The Limitation and Practical Acceleration of Stochastic Gradient Algorithms in Inverse Problems. - Junqi Tang; Karen O. Egiazarian; Mike E. Davies 001 (ICASSP, 2019)

Randomized structure-adaptive optimization. - Junqi Tang (2019)

CNN Denoisers as Non-Local Filters: The Neural Tangent Denoiser. - Julián Tachella; Junqi Tang; Mike E. Davies 001 (CoRR, 2020)

A Fast Stochastic Plug-and-Play ADMM for Imaging Inverse Problems. - Junqi Tang; Mike E. Davies 001 (CoRR, 2020)

The Practicality of Stochastic Optimization in Imaging Inverse Problems. - Junqi Tang; Karen O. Egiazarian; Mohammad Golbabaee; Mike E. Davies 001 (IEEE Trans. Computational Imaging, 2020)

The Neural Tangent Link Between CNN Denoisers and Non-Local Filters. - Julián Tachella; Junqi Tang; Mike E. Davies 001 (CVPR, 2021)

Stochastic Primal-Dual Deep Unrolling Networks for Imaging Inverse Problems. - Junqi Tang (CoRR, 2021)

A Stochastic Proximal Alternating Minimization for Nonsmooth and Nonconvex Optimization. - Derek Driggs; Junqi Tang; Jingwei Liang; Mike E. Davies 001; Carola-Bibiane Schönlieb (SIAM J. Imaging Sci., 2021)

Equivariance Regularization for Image Reconstruction. - Junqi Tang (CoRR, 2022)

Data-Consistent Local Superresolution for Medical Imaging. - Junqi Tang (CoRR, 2022)

Accelerating Plug-and-Play Image Reconstruction via Multi-Stage Sketched Gradients. - Junqi Tang (CoRR, 2022)

Operator Sketching for Deep Unrolling Networks. - Junqi Tang (CoRR, 2022)

Stochastic Primal-Dual Three Operator Splitting with Arbitrary Sampling and Preconditioning. - Junqi Tang; Matthias J. Ehrhardt; Carola-Bibiane Schönlieb (CoRR, 2022)

Accelerating Deep Unrolling Networks via Dimensionality Reduction. - Junqi Tang; Subhadip Mukherjee; Carola-Bibiane Schönlieb (CoRR, 2022)

Robust Data-Driven Accelerated Mirror Descent. - Hong Ye Tan; Subhadip Mukherjee; Junqi Tang; Andreas Hauptmann; Carola-Bibiane Schönlieb (ICASSP, 2023)

NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problems. - Ziruo Cai; Junqi Tang; Subhadip Mukherjee; Jinglai Li; Carola-Bibiane Schönlieb; Xiaoqun Zhang (CoRR, 2023)

Deep Unrolling Networks with Recurrent Momentum Acceleration for Nonlinear Inverse Problems. - Qingping Zhou; Jiayu Qian; Junqi Tang; Jinglai Li (CoRR, 2023)

Unsupervised approaches based on optimal transport and convex analysis for inverse problems in imaging. - Marcello Carioni; Subhadip Mukherjee; Hong Ye Tan; Junqi Tang (CoRR, 2023)

OsmoticGate: Adaptive Edge-Based Real-Time Video Analytics for the Internet of Things. - Bin Qian 002; Zhenyu Wen; Junqi Tang; Ye Yuan 001; Albert Y. Zomaya; Rajiv Ranjan 001 (IEEE Trans. Computers, 2023)

Data-Driven Mirror Descent with Input-Convex Neural Networks. - Hong Ye Tan; Subhadip Mukherjee; Junqi Tang; Carola-Bibiane Schönlieb (SIAM J. Math. Data Sci., 2023)

A Guide to Stochastic Optimisation for Large-Scale Inverse Problems. - Matthias J. Ehrhardt; Zeljko Kereta; Jingwei Liang; Junqi Tang (CoRR, 2024)

Blessing of Dimensionality for Approximating Sobolev Classes on Manifolds. - Hong Ye Tan; Subhadip Mukherjee; Junqi Tang; Carola-Bibiane Schönlieb (CoRR, 2024)

Sketched Equivariant Imaging Regularization and Deep Internal Learning for Inverse Problems. - Guixian Xu; Jinglai Li; Junqi Tang (CoRR, 2024)

NF-ULA: Normalizing Flow-Based Unadjusted Langevin Algorithm for Imaging Inverse Problems. - Ziruo Cai; Junqi Tang; Subhadip Mukherjee; Jinglai Li; Carola-Bibiane Schönlieb; Xiaoqun Zhang (SIAM J. Imaging Sci., 2024)

Practical Acceleration of the Condat-Vũ Algorithm. - Derek Driggs; Matthias J. Ehrhardt; Carola-Bibiane Schönlieb; Junqi Tang (SIAM J. Imaging Sci., 2024)

Provably Convergent Plug-and-Play Quasi-Newton Methods. - Hong Ye Tan; Subhadip Mukherjee; Junqi Tang; Carola-Bibiane Schönlieb (SIAM J. Imaging Sci., 2024)

Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation. - Hong Ye Tan; Ziruo Cai; Marcelo Pereyra; Subhadip Mukherjee; Junqi Tang; Carola-Bibiane Schönlieb (Trans. Mach. Learn. Res., 2024)

Boosting Data-Driven Mirror Descent with Randomization, Equivariance, and Acceleration. - Hong Ye Tan; Subhadip Mukherjee; Junqi Tang; Carola-Bibiane Schönlieb (Trans. Mach. Learn. Res., 2024)

Stochastic Primal-Dual Hybrid Gradient Algorithm with Adaptive Step Sizes. - Antonin Chambolle; Claire Delplancke; Matthias J. Ehrhardt; Carola-Bibiane Schönlieb; Junqi Tang (J. Math. Imaging Vis., 2024)

Iterative Operator Sketching Framework for Large-Scale Imaging Inverse Problems. - Junqi Tang; Subhadip Mukherjee; Carola-Bibiane Schönlieb (ICASSP, 2025)

Stochastic Primal-Dual Three Operator Splitting Algorithm with Extension to Equivariant Regularization-by-Denoising. - Junqi Tang; Matthias J. Ehrhardt; Carola-Bibiane Schönlieb (SSVM (2), 2025)

Fast Equivariant Imaging: Acceleration for Unsupervised Learning via Augmented Lagrangian and Auxiliary PnP Denoisers. - Guixian Xu; Jinglai Li; Junqi Tang (CoRR, 2025)

Practical Operator Sketching Framework for Accelerating Iterative Data-Driven Solutions in Linear Inverse Problems. - Junqi Tang; Guixian Xu; Subhadip Mukherjee; Carola-Bibiane Schönlieb (J. Math. Imaging Vis., 2025)