Ding, Mucong

17 publications

NeurIPS 2025 A Technical Report on “Erasing the Invisible”: The 2024 NeurIPS Competition on Stress Testing Image Watermarks Mucong Ding, Bang An, Tahseen Rabbani, Chenghao Deng, Anirudh Satheesh, Souradip Chakraborty, Mehrdad Saberi, Yuxin Wen, Kyle Rui Sang, Aakriti Agrawal, Xuandong Zhao, Mo Zhou, Mary-Anne Hartley, Lei Li, Yu-Xiang Wang, Vishal M. Patel, Soheil Feizi, Tom Goldstein, Furong Huang

TMLR 2025 PICore: Physics-Informed Unsupervised Coreset Selection for Data Efficient Neural Operator Training Anirudh Satheesh, Anant Khandelwal, Mucong Ding, Radu Balan

NeurIPS 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

NeurIPSW 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

ICLR 2024 SAFLEX: Self-Adaptive Augmentation via Feature Label Extrapolation Mucong Ding, Bang An, Yuancheng Xu, Anirudh Satheesh, Furong Huang

ICMLW 2024 SAIL: Self-Improving Efficient Online Alignment of Large Language Models Mucong Ding, Souradip Chakraborty, Vibhu Agrawal, Zora Che, Alec Koppel, Mengdi Wang, Amrit Bedi, Furong Huang

ICML 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

ICLRW 2024 WAVES: Benchmarking the Robustness of Image Watermarks Mucong Ding, Tahseen Rabbani, Bang An, Aakriti Agrawal, Yuancheng Xu, Chenghao Deng, Sicheng Zhu, Abdirisak Mohamed, Yuxin Wen, Tom Goldstein, Furong Huang

NeurIPSW 2022 Faster Hyperparameter Search on Graphs via Calibrated Dataset Condensation Mucong Ding, Xiaoyu Liu, Tahseen Rabbani, Furong Huang

CVPR 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

NeurIPS 2022 Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity Mucong Ding, Tahseen Rabbani, Bang An, Evan Wang, Furong Huang

NeurIPSW 2022 Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity Mucong Ding, Tahseen Rabbani, Bang An, Evan Z Wang, Furong Huang

NeurIPS 2022 Transferring Fairness Under Distribution Shifts via Fair Consistency Regularization Bang An, Zora Che, Mucong Ding, Furong Huang

AISTATS 2021 GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences Mucong Ding, Constantinos Daskalakis, Soheil Feizi

NeurIPSW 2021 A Closer Look at Distribution Shifts and Out-of-Distribution Generalization on Graphs Mucong Ding, Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Micah Goldblum, David Wipf, Furong Huang, Tom Goldstein

ICLR 2021 Understanding Over-Parameterization in Generative Adversarial Networks Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi

NeurIPS 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