| Plug-and-play methods provably converge with properly trained denoisers E Ryu, J Liu, S Wang, X Chen, Z Wang, W Yin International Conference on Machine Learning, 5546-5557, 2019 | 431 | 2019 |
| Theoretical linear convergence of unfolded ISTA and its practical weights and thresholds X Chen, J Liu, Z Wang, W Yin Advances in Neural Information Processing Systems 31, 2018 | 285 | 2018 |
| Learning to optimize: A primer and a benchmark T Chen, X Chen, W Chen, H Heaton, J Liu, Z Wang, W Yin Journal of Machine Learning Research 23 (189), 1-59, 2022 | 255 | 2022 |
| ALISTA: Analytic weights are as good as learned weights in LISTA J Liu, X Chen International Conference on Learning Representations (ICLR), 2019 | 221 | 2019 |
| Learning convolutional sparse coding on complex domain for interferometric phase restoration J Kang, D Hong, J Liu, G Baier, N Yokoya, B Demir IEEE transactions on neural networks and learning systems 32 (2), 826-840, 2020 | 70 | 2020 |
| Learned robust PCA: A scalable deep unfolding approach for high-dimensional outlier detection HQ Cai, J Liu, W Yin Advances in Neural Information Processing Systems 34, 16977-16989, 2021 | 49 | 2021 |
| First-and second-order methods for online convolutional dictionary learning J Liu, C Garcia-Cardona, B Wohlberg, W Yin SIAM Journal on Imaging Sciences 11 (2), 1589-1628, 2018 | 49 | 2018 |
| On representing linear programs by graph neural networks Z Chen, J Liu, X Wang, J Lu, W Yin International Conference on Learning Representations (ICLR), 2023 | 48 | 2023 |
| Online convolutional dictionary learning J Liu, C Garcia-Cardona, B Wohlberg, W Yin 2017 IEEE International Conference on Image Processing (ICIP), 1707-1711, 2017 | 43 | 2017 |
| Multilevel optimal transport: a fast approximation of Wasserstein-1 distances J Liu, W Yin, W Li, YT Chow SIAM Journal on Scientific Computing, 2021 | 38 | 2021 |
| Learning a minimax optimizer: A pilot study J Shen, X Chen, H Heaton, T Chen, J Liu, W Yin, Z Wang International Conference on Learning Representations, 2020 | 32 | 2020 |
| Hyperparameter tuning is all you need for LISTA X Chen, J Liu, Z Wang, W Yin Advances in Neural Information Processing Systems 34, 11678-11689, 2021 | 28 | 2021 |
| Towards Constituting Mathematical Structures for Learning to Optimize J Liu, X Chen, Z Wang, W Yin, HQ Cai International Conference on Machine Learning, 2023 | 14 | 2023 |
| Coherence-guided complex convolutional sparse coding for interferometric phase restoration X Ding, J Kang, Z Zhang, Y Huang, J Liu, N Yokoya IEEE Transactions on Geoscience and Remote Sensing 60, 1-14, 2022 | 7 | 2022 |
| Averaging random projection: A fast online solution for large-scale constrained stochastic optimization J Liu, Y Gu, M Wang 2015 IEEE International Conference on Acoustics, Speech and Signal …, 2015 | 5 | 2015 |
| Learning to optimize: A tutorial for continuous and mixed-integer optimization X Chen, J Liu, W Yin Science China Mathematics, 1-72, 2024 | 4 | 2024 |
| A Mathematics-Inspired Learning-to-Optimize Framework for Decentralized Optimization Y He, Q Shang, X Huang, J Liu, K Yuan arXiv preprint arXiv:2410.01700, 2024 | 2 | 2024 |
| Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs Z Chen, X Chen, J Liu, X Wang, W Yin arXiv preprint arXiv:2406.05938, 2024 | 2 | 2024 |
| Rethinking the capacity of graph neural networks for branching strategy Z Chen, J Liu, X Chen, X Wang, W Yin arXiv preprint arXiv:2402.07099, 2024 | 2 | 2024 |
| DIG-MILP: a Deep Instance Generator for Mixed-Integer Linear Programming with Feasibility Guarantee H Wang, J Liu, X Chen, X Wang, P Li, W Yin arXiv preprint arXiv:2310.13261, 2023 | 2 | 2023 |
Wotao YinDAMO Academy, Alibaba Group US確認したメール アドレス: alibaba-inc.com