Mucong Ding

According to our database, Mucong Ding authored at least 21 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

EnsemW2S: Can an Ensemble of LLMs be Leveraged to Obtain a Stronger LLM?

Aakriti Agrawal, Mucong Ding, Zora Che, Chenghao Deng, Anirudh Satheesh, John Langford, Furong Huang

CoRR, 2024

Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization.

Mucong Ding, Chenghao Deng, Jocelyn Choo, Zichu Wu, Aakriti Agrawal, Avi Schwarzschild, Tianyi Zhou, Tom Goldstein, John Langford, Anima Anandkumar, Furong Huang

CoRR, 2024

Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity.

Mucong Ding, Tahseen Rabbani, Bang An, Evan Z. Wang, Furong Huang

CoRR, 2024

SAIL: Self-Improving Efficient Online Alignment of Large Language Models.

Mucong Ding, Souradip Chakraborty, Vibhu Agrawal, Zora Che, Alec Koppel, Mengdi Wang, Amrit S. Bedi, Furong Huang

CoRR, 2024

Calibrated Dataset Condensation for Faster Hyperparameter Search.

Mucong Ding, Yuancheng Xu, Tahseen Rabbani, Xiaoyu Liu, Brian J. Gravelle, Teresa M. Ranadive, Tai-Ching Tuan, Furong Huang

CoRR, 2024

Spectral Greedy Coresets for Graph Neural Networks.

Mucong Ding, Yinhan He, Jundong Li, Furong Huang

CoRR, 2024

Benchmarking the Robustness of Image Watermarks.

Bang An, Mucong Ding, Tahseen Rabbani, Aakriti Agrawal, Yuancheng Xu, Chenghao Deng, Sicheng Zhu, Abdirisak Mohamed, Yuxin Wen, Tom Goldstein, Furong Huang

CoRR, 2024

WAVES: Benchmarking the Robustness of Image Watermarks.

Bang An, Mucong Ding, Tahseen Rabbani, Aakriti Agrawal, Yuancheng Xu, Chenghao Deng, Sicheng Zhu, Abdirisak Mohamed, Yuxin Wen, Tom Goldstein, Furong Huang

Proceedings of the Forty-first International Conference on Machine Learning, 2024

SAFLEX: Self-Adaptive Augmentation via Feature Label Extrapolation.

Mucong Ding, Bang An, Yuancheng Xu, Anirudh Satheesh, Furong Huang

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2022

Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity.

Mucong Ding, Tahseen Rabbani, Bang An, Evan Wang, Furong Huang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Transferring Fairness under Distribution Shifts via Fair Consistency Regularization.

Bang An, Zora Che, Mucong Ding, Furong Huang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Robust Optimization as Data Augmentation for Large-scale Graphs.

Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

Understanding Overparameterization in Generative Adversarial Networks.

Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi

CoRR, 2021

VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization.

Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John Dickerson, Furong Huang, Tom Goldstein

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Understanding Over-parameterization in Generative Adversarial Networks.

Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi

Proceedings of the 9th International Conference on Learning Representations, 2021

GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences.

Mucong Ding, Constantinos Daskalakis, Soheil Feizi

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

FLAG: Adversarial Data Augmentation for Graph Neural Networks.

Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein

CoRR, 2020

Subadditivity of Probability Divergences on Bayes-Nets with Applications to Time Series GANs.

Mucong Ding, Constantinos Daskalakis, Soheil Feizi

CoRR, 2020

2019

Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses.

Mucong Ding, Kai Yang, Dit-Yan Yeung, Ting-Chuen Pong

Proceedings of the 9th International Conference on Learning Analytics & Knowledge, 2019

Transfer Learning using Representation Learning in Massive Open Online Courses.

Mucong Ding, Yanbang Wang, Erik Hemberg, Una-May O'Reilly

Proceedings of the 9th International Conference on Learning Analytics & Knowledge, 2019

2018

First-passage time distribution for random walks on complex networks using inverse Laplace transform and mean-field approximation.

Mucong Ding, Kwok Yip Szeto

CoRR, 2018