Mucong Ding

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 22

Top Topics

Unsupervised Learning

Vector Quantization

Conditional Independencies

Top Venues

Publications

Mucong Ding, Tahseen Rabbani, Bang An, Evan Z. Wang, Furong Huang
Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity. CoRR (2024)
Mucong Ding, Yinhan He, Jundong Li, Furong Huang
Spectral Greedy Coresets for Graph Neural Networks. CoRR (2024)
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 (2024)
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 (2024)
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 (2024)
Mucong Ding, Bang An, Yuancheng Xu, Anirudh Satheesh, Furong Huang
SAFLEX: Self-Adaptive Augmentation via Feature Label Extrapolation. ICLR (2024)
Bang An, Zora Che, Mucong Ding, Furong Huang
Transferring Fairness under Distribution Shifts via Fair Consistency Regularization. CoRR (2022)
Mucong Ding, Tahseen Rabbani, Bang An, Evan Wang, Furong Huang
Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity. NeurIPS (2022)
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)
Bang An, Zora Che, Mucong Ding, Furong Huang
Transferring Fairness under Distribution Shifts via Fair Consistency Regularization. NeurIPS (2022)
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 (2021)
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)
Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi
Understanding Overparameterization in Generative Adversarial Networks. CoRR (2021)
Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi
Understanding Over-parameterization in Generative Adversarial Networks. ICLR (2021)
Mucong Ding, Constantinos Daskalakis, Soheil Feizi
GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences. AISTATS (2021)
Mucong Ding, Constantinos Daskalakis, Soheil Feizi
Subadditivity of Probability Divergences on Bayes-Nets with Applications to Time Series GANs. CoRR (2020)
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 (2020)
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)
Mucong Ding, Yanbang Wang, Erik Hemberg, Una-May O'Reilly
Transfer Learning using Representation Learning in Massive Open Online Courses. LAK (2019)
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 (2018)
Mucong Ding, Yanbang Wang, Erik Hemberg, Una-May O'Reilly
Transfer Learning using Representation Learning in Massive Open Online Courses. CoRR (2018)
Mucong Ding, Kwok Yip Szeto
First-passage time distribution for random walks on complex networks using inverse Laplace transform and mean-field approximation. CoRR (2018)