| Flag: Adversarial data augmentation for graph neural networks K Kong, G Li, M Ding, Z Wu, C Zhu, B Ghanem, G Taylor, T Goldstein arXiv, 2020 | 123 | 2020 |
| Robust optimization as data augmentation for large-scale graphs K Kong, G Li, M Ding, Z Wu, C Zhu, B Ghanem, G Taylor, T Goldstein Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 84 | 2022 |
| VQ-GNN: A universal framework to scale up graph neural networks using vector quantization M Ding, K Kong, J Li, C Zhu, J Dickerson, F Huang, T Goldstein Advances in Neural Information Processing Systems 34, 6733-6746, 2021 | 48* | 2021 |
| Understanding overparameterization in generative adversarial networks Y Balaji, M Sajedi, NM Kalibhat, M Ding, D Stöger, M Soltanolkotabi, ... arXiv preprint arXiv:2104.05605, 2021 | 34 | 2021 |
| Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses M Ding, K Yang, DY Yeung, TC Pong International Learning Analytics and Knowledge (LAK'19), 2019 | 32 | 2019 |
| Transferring fairness under distribution shifts via fair consistency regularization B An, Z Che, M Ding, F Huang Advances in Neural Information Processing Systems 35, 32582-32597, 2022 | 31 | 2022 |
| Transfer Learning using Representation Learning in Massive Open Online Courses M Ding, Y Wang, E Hemberg, UM O'Reilly International Learning Analytics and Knowledge (LAK'19), 2019 | 28 | 2019 |
| Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Micah Goldblum, David Wipf, Furong Huang, and Tom Goldstein. A closer look at distribution shifts and out-of-distribution … M Ding NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and …, 2021 | 19 | 2021 |
| A closer look at distribution shifts and out-of-distribution generalization on graphs M Ding, K Kong, J Chen, J Kirchenbauer, M Goldblum, D Wipf, F Huang, ... | 18 | 2021 |
| Sketch-GNN: Scalable graph neural networks with sublinear training complexity M Ding, T Rabbani, B An, E Wang, F Huang Advances in Neural Information Processing Systems 35, 2930-2943, 2022 | 12 | 2022 |
| Benchmarking the robustness of image watermarks B An, M Ding, T Rabbani, A Agrawal, Y Xu, C Deng, S Zhu, A Mohamed, ... arXiv preprint arXiv:2401.08573, 2024 | 11 | 2024 |
| Gans with conditional independence graphs: On subadditivity of probability divergences M Ding, C Daskalakis, S Feizi International Conference on Artificial Intelligence and Statistics, 3709-3717, 2021 | 10 | 2021 |
| FLAG: adversarial data augmentation for graph neural networks 2020 K Kong, G Li, M Ding, Z Wu, C Zhu, B Ghanem, G Taylor, T Goldstein arXiv preprint arXiv:2010.09891, 2021 | 9 | 2021 |
| Faster hyperparameter search on graphs via calibrated dataset condensation M Ding, X Liu, T Rabbani, F Huang NeurIPS 2022 Workshop: New Frontiers in Graph Learning, 2022 | 5 | 2022 |
| First-passage time distribution for random walks on complex networks using inverse Laplace transform and mean-field approximation M Ding, KY Szeto | 3 | 2018 |
| Selection of random walkers that optimizes the global mean first-passage time for search in complex networks MC Ding, KY Szeto Procedia Computer Science 108, 2423-2427, 2017 | 2 | 2017 |
| Spectral Greedy Coresets for Graph Neural Networks M Ding, Y He, J Li, F Huang arXiv preprint arXiv:2405.17404, 2024 | 1 | 2024 |
| Faster Hyperparameter Search for GNNs via Calibrated Dataset Condensation M Ding, X Liu, T Rabbani, T Ranadive, TC Tuan, F Huang | 1 | 2022 |
| SAIL: Self-Improving Efficient Online Alignment of Large Language Models M Ding, S Chakraborty, V Agrawal, Z Che, A Koppel, M Wang, A Bedi, ... arXiv preprint arXiv:2406.15567, 2024 | | 2024 |
| Calibrated Dataset Condensation for Faster Hyperparameter Search M Ding, Y Xu, T Rabbani, X Liu, B Gravelle, T Ranadive, TC Tuan, ... arXiv preprint arXiv:2405.17535, 2024 | | 2024 |
Tom GoldsteinVolpi-Cupal Professor of Computer Science, University of Maryland在 cs.umd.edu 的电子邮件经过验证
