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Mucong Ding
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2020 – today
- 2024
- [c9]Mucong Ding, Bang An, Yuancheng Xu, Anirudh Satheesh, Furong Huang:
SAFLEX: Self-Adaptive Augmentation via Feature Label Extrapolation. ICLR 2024 - [i13]Bang An, Mucong Ding, Tahseen Rabbani, Aakriti Agrawal, Yuancheng Xu, Chenghao Deng, Sicheng Zhu, Abdirisak Mohamed, Yuxin Wen, Tom Goldstein, Furong Huang:
Benchmarking the Robustness of Image Watermarks. CoRR abs/2401.08573 (2024) - [i12]Mucong Ding, Yinhan He, Jundong Li, Furong Huang:
Spectral Greedy Coresets for Graph Neural Networks. CoRR abs/2405.17404 (2024) - [i11]Mucong Ding, Yuancheng Xu, Tahseen Rabbani, Xiaoyu Liu, Brian J. Gravelle, Teresa M. Ranadive, Tai-Ching Tuan, Furong Huang:
Calibrated Dataset Condensation for Faster Hyperparameter Search. CoRR abs/2405.17535 (2024) - [i10]Mucong Ding, Souradip Chakraborty, Vibhu Agrawal, Zora Che, Alec Koppel, Mengdi Wang, Amrit S. Bedi, Furong Huang:
SAIL: Self-Improving Efficient Online Alignment of Large Language Models. CoRR abs/2406.15567 (2024) - [i9]Mucong Ding, Tahseen Rabbani, Bang An, Evan Z. Wang, Furong Huang:
Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity. CoRR abs/2406.15575 (2024) - 2022
- [c8]Kezhi Kong, Guohao Li
, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem
, Gavin Taylor, Tom Goldstein:
Robust Optimization as Data Augmentation for Large-scale Graphs. CVPR 2022: 60-69 - [c7]Bang An, Zora Che, Mucong Ding, Furong Huang:
Transferring Fairness under Distribution Shifts via Fair Consistency Regularization. NeurIPS 2022 - [c6]Mucong Ding, Tahseen Rabbani, Bang An, Evan Wang, Furong Huang:
Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity. NeurIPS 2022 - [i8]Bang An, Zora Che, Mucong Ding, Furong Huang:
Transferring Fairness under Distribution Shifts via Fair Consistency Regularization. CoRR abs/2206.12796 (2022) - 2021
- [c5]Mucong Ding, Constantinos Daskalakis, Soheil Feizi:
GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences. AISTATS 2021: 3709-3717 - [c4]Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi
, Soheil Feizi:
Understanding Over-parameterization in Generative Adversarial Networks. ICLR 2021 - [c3]Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John Dickerson, Furong Huang, Tom Goldstein:
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization. NeurIPS 2021: 6733-6746 - [i7]Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi:
Understanding Overparameterization in Generative Adversarial Networks. CoRR abs/2104.05605 (2021) - [i6]Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John P. Dickerson, Furong Huang, Tom Goldstein:
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization. CoRR abs/2110.14363 (2021) - 2020
- [i5]Mucong Ding, Constantinos Daskalakis, Soheil Feizi:
Subadditivity of Probability Divergences on Bayes-Nets with Applications to Time Series GANs. CoRR abs/2003.00652 (2020) - [i4]Kezhi Kong, Guohao Li
, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem
, Gavin Taylor, Tom Goldstein:
FLAG: Adversarial Data Augmentation for Graph Neural Networks. CoRR abs/2010.09891 (2020)
2010 – 2019
- 2019
- [c2]Mucong Ding, Kai Yang, Dit-Yan Yeung, Ting-Chuen Pong:
Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses. LAK 2019: 135-144 - [c1]Mucong Ding, Yanbang Wang
, Erik Hemberg, Una-May O'Reilly:
Transfer Learning using Representation Learning in Massive Open Online Courses. LAK 2019: 145-154 - 2018
- [i3]Mucong Ding, Yanbang Wang, Erik Hemberg, Una-May O'Reilly:
Transfer Learning using Representation Learning in Massive Open Online Courses. CoRR abs/1812.05043 (2018) - [i2]Mucong Ding, Kai Yang, Dit-Yan Yeung, Ting-Chuen Pong:
Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses. CoRR abs/1812.05044 (2018) - [i1]Mucong Ding, Kwok Yip Szeto:
First-passage time distribution for random walks on complex networks using inverse Laplace transform and mean-field approximation. CoRR abs/1812.05598 (2018)
Coauthor Index
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last updated on 2024-08-08 20:10 CEST by the dblp team
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