Recent publications
Article
Tan, HY, Mukherjee, S, Tang, J & Schönlieb, C-B 2024, 'Boosting Data-Driven Mirror Descent with Randomization, Equivariance, and Acceleration', Transactions on Machine Learning Research. <https://openreview.net/pdf?id=r2dx1s1lqG>
Zhou, Q, Qian, J, Tang, J & Li, J 2024, 'Deep unrolling networks with recurrent momentum acceleration for nonlinear inverse problems', Inverse Problems, vol. 40, no. 5, 055014. https://doi.org/10.1088/1361-6420/ad35e3
Cai, Z, Tang, J, Mukherjee, S, Li, J, Schönlieb, C-B & Zhang, X 2024, 'NF-ULA: Normalizing Flow-Based Unadjusted Langevin Algorithm for Imaging Inverse Problems', SIAM Journal on Imaging Sciences, vol. 17, no. 2, pp. 820-860. https://doi.org/10.1137/23M1581807
Driggs, D, Ehrhardt, M, Schönlieb, C-B & Tang, J 2024, 'Practical Acceleration of the Condat–Vũ Algorithm', SIAM Journal on Imaging Sciences.
Tan, HY, Mukherjee, S, Tang, J & Schönlieb, C-B 2024, 'Provably Convergent Plug-and-Play Quasi-Newton Methods', SIAM Journal on Imaging Sciences, vol. 17, no. 2, pp. 785-819. https://doi.org/10.1137/23M157185X
Chambolle, A, Delplancke, C, Ehrhardt, M, Schönlieb, C-B & Tang, J 2024, 'Stochastic Primal-Dual Hybrid Gradient Algorithm with Adaptive Step Sizes', Journal of Mathematical Imaging and Vision. https://doi.org/10.1007/s10851-024-01174-1
Tan, HY, Mukherjee, S, Tang, J & Schönlieb, C-B 2023, 'Data-Driven Mirror Descent with Input-Convex Neural Networks', SIAM Journal on Mathematics of Data Science, vol. 5, no. 2, pp. 558-587. https://doi.org/10.1137/22M1508613
Qian, B, Wen, Z, Tang, J, Yuan, Y, Zomaya, A & Ranjan, R 2023, 'OsmoticGate: Adaptive Edge-based Real-time Video Analytics for the Internet of Things', IEEE Transactions on Computers, vol. 72, no. 4, pp. 1178-1193. https://doi.org/10.1109/TC.2022.3193630
Driggs, D, Tang, J, Liang, J, Davies, M & Schönlieb, C-B 2021, 'A Stochastic Proximal Alternating Minimization for Nonsmooth and Nonconvex Optimization', SIAM Journal on Imaging Sciences, vol. 14, no. 4, pp. 1932-1970. https://doi.org/10.1137/20M1387213
Tang, J, Egiazarian, K, Golbabaee, M & Davies, M 2020, 'The Practicality of Stochastic Optimization in Imaging Inverse Problems', IEEE Transactions on Computational Imaging. https://doi.org/10.1109/TCI.2020.3032101
Conference contribution
Tan, HY, Mukherjee, S, Tang, J, Hauptmann, A & Schönlieb, C-B 2023, Robust Data-Driven Accelerated Mirror Descent. in ICASSP 2023 - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Greece, 4/06/23. https://doi.org/10.1109/ICASSP49357.2023.10096875
Tachella, J, Tang, J & Davies, M 2021, The Neural Tangent Link Between CNN Denoisers and Non-Local Filters. in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)., 9578172, Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, pp. 8614-8623, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, Tennessee, United States, 20/06/21. https://doi.org/10.1109/CVPR46437.2021.00851
Tang, J, Egiazarian, K & Davies, M 2019, The Limitation and Practical Acceleration of Stochastic Gradient Algorithms in Inverse Problems. in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/ICASSP.2019.8683368
Tang, J, Golbabaee, M, Bach, F & Davies, M 2018, Rest-Katyusha: Exploiting the Solution’s Structure via Scheduled Restart Schemes. in Advances in Neural Information Processing Systems 31 (NeurIPS 2018). <https://proceedings.neurips.cc/paper_files/paper/2018/file/39059724f73a9969845dfe4146c5660e-Paper.pdf>
Tang, J, Golbabaee, M & Davies, M 2017, Exploiting the structure via sketched gradient algorithms. in 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP). https://doi.org/10.1109/GlobalSIP.2017.8309172
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