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Mucong Ding
Mucong Ding
Department of Computer Science, University of Maryland
Geverifieerd e-mailadres voor cs.umd.edu - Homepage
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Flag: Adversarial data augmentation for graph neural networks
K Kong, G Li, M Ding, Z Wu, C Zhu, B Ghanem, G Taylor, T Goldstein
1132020
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
662022
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
34*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
332021
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
312019
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
262019
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
212022
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
162021
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, ...
142021
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
102021
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
82022
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
72021
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
32022
First-passage time distribution for random walks on complex networks using inverse Laplace transform and mean-field approximation
M Ding, KY Szeto
32018
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
22017
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
12024
Spectral Greedy Coresets for Graph Neural Networks
M Ding, Y He, J Li, F Huang
2023
Calibrated Dataset Condensation for Faster Hyperparameter Search
M Ding, Y Xu, T Rabbani, X Liu, B Gravelle, T Ranadive, F Huang
2023
SAFLEX: Self-Adaptive Augmentation via Feature Label Extrapolation
M Ding, B An, Y Xu, A Satheesh, F Huang
The Twelfth International Conference on Learning Representations, 2023
2023
Faster Hyperparameter Search for GNNs via Calibrated Dataset Condensation
M Ding, X Liu, T Rabbani, T Ranadive, TC Tuan, F Huang
2022
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Artikelen 1–20