| PDE-Net: Learning PDEs from Data Z Long, Y Lu, X Ma, B Dong Thirty-fifth International Conference on Machine Learning (ICML), 2018. 80 …, 2017 | 800 | 2017 |
| Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations Y Lu, A Zhong, Q Li, B Dong Thirty-fifth International Conference on Machine Learning (ICML), 2018. 80 …, 2017 | 551 | 2017 |
| PDE-Net 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network Z Long, Y Lu, B Dong Journal of Computational Physics 399, 108925, 2019 | 538 | 2019 |
| You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle D Zhang*, T Zhang*, Y Lu*, Z Zhu, B Dong 33rd Annual Conference on Neural Information Processing Systems 2019 …, 2019 | 432 | 2019 |
| Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View Y Lu, Z Li, D He, Z Sun, B Dong, T Qin, L Wang, TY Liu International Conference on Learning Representations 2020 Workshop on …, 2019 | 154 | 2019 |
| A mean-field analysis of deep resnet and beyond: Towards provable optimization via overparameterization from depth Y Lu, C Ma, Y Lu, J Lu, L Ying Thirty-seventh International Conference on Machine Learning(ICML2020), 2020 | 82 | 2020 |
| An unconstrained layer-peeled perspective on neural collapse W Ji, Y Lu, Y Zhang, Z Deng, WJ Su arXiv preprint arXiv:2110.02796, 2021 | 61 | 2021 |
| Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration X Zhang*, Y Lu*, J Liu, B Dong Seventh International Conference on Learning Representations(ICLR) 2019, 2018 | 55 | 2018 |
| Machine learning for elliptic pdes: fast rate generalization bound, neural scaling law and minimax optimality Y Lu, H Chen, J Lu, L Ying, J Blanchet arXiv preprint arXiv:2110.06897, 2021 | 31 | 2021 |
| Distillation Early Stopping? Harvesting Dark Knowledge Utilizing Anisotropic Information Retrieval For Overparameterized Neural Network B Dong, J Hou, Y Lu, Z Zhang Conference on Neural Information Processing Systems 2019 Workshop on ML with …, 2019 | 30 | 2019 |
| CURE: Curvature regularization for missing data recovery B Dong, H Ju, Y Lu, Z Shi SIAM Journal on Imaging Sciences 13 (4), 2169-2188, 2020 | 13 | 2020 |
| Sobolev acceleration and statistical optimality for learning elliptic equations via gradient descent Y Lu, J Blanchet, L Ying Advances in Neural Information Processing Systems 35, 33233-33247, 2022 | 8 | 2022 |
| Minimax optimal kernel operator learning via multilevel training J Jin, Y Lu, J Blanchet, L Ying The Eleventh International Conference on Learning Representations, 2022 | 8 | 2022 |
| Importance tempering: Group robustness for overparameterized models Y Lu, W Ji, Z Izzo, L Ying arXiv preprint arXiv:2209.08745, 2022 | 5 | 2022 |
| Generalization Guarantees of Deep ResNets in the Mean-Field Regime Y Chen, F Liu, Y Lu, G Chrysos, V Cevher NeurIPS 2023 Workshop on Mathematics of Modern Machine Learning, 2023 | 1 | 2023 |
| Synthetic Principal Component Design: Fast Covariate Balancing with Synthetic Controls Y Lu, J Li, L Ying, J Blanchet arXiv preprint arXiv:2211.15241, 2022 | 1 | 2022 |
| Statistical Spatially Inhomogeneous Diffusion Inference Y Ren, Y Lu, L Ying, GM Rotskoff Proceedings of the AAAI Conference on Artificial Intelligence 38 (13), 14820 …, 2024 | | 2024 |
| Generalization of Scaled Deep ResNets in the Mean-Field Regime Y Chen, F Liu, Y Lu, GG Chrysos, V Cevher arXiv preprint arXiv:2403.09889, 2024 | | 2024 |
| Representation of General Continuous Anti-symmetric Function H Ye, R Li, Y Gu, Y Lu, D He, L Wang arXiv preprint arXiv:2402.15167, 2024 | | 2024 |
| When can Regression-Adjusted Control Variate Help? Rare Events, Sobolev Embedding and Minimax Optimality J Blanchet, H Chen, Y Lu, L Ying Advances in Neural Information Processing Systems 36, 2024 | | 2024 |
Bin DongBeijing International Center for Mathematical Research, Peking UniversityVerified email at math.pku.edu.cn
