Kezhi Kong - RankMe

21 papers A* 8Journal 10Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	—	`conf`	Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset. ACL (1) Dan Su, Kezhi Kong, Ying Lin, Joseph Jennings, Brandon Norick, Markus Kliegl, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro
2024	J	`jnl`	Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase Pretraining. CoRR Steven Feng, Shrimai Prabhumoye, Kezhi Kong, Dan Su, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro
2024	J	`jnl`	Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset. CoRR Dan Su, Kezhi Kong, Ying Lin, Joseph Jennings, Brandon Norick, Markus Kliegl, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro
2024	A*	`conf`	On the Reliability of Watermarks for Large Language Models. ICLR John Kirchenbauer, Jonas Geiping, Yuxin Wen, Manli Shu, Khalid Saifullah, Kezhi Kong, Kasun Fernando, Aniruddha Saha, Micah Goldblum, Tom Goldstein
2024	A*	`conf`	OpenTab: Advancing Large Language Models as Open-domain Table Reasoners. ICLR Kezhi Kong, Jiani Zhang, Zhengyuan Shen, Balasubramaniam Srinivasan, Chuan Lei, Christos Faloutsos, Huzefa Rangwala, George Karypis
2024	J	`jnl`	OpenTab: Advancing Large Language Models as Open-domain Table Reasoners. CoRR Kezhi Kong, Jiani Zhang, Zhengyuan Shen, Balasubramaniam Srinivasan, Chuan Lei, Christos Faloutsos, Huzefa Rangwala, George Karypis
2024	—	`—`	Towards Generalized and Scalable Machine Learning on Structured Data. Kezhi Kong
2023	A*	`conf`	GOAT: A Global Transformer on Large-scale Graphs. ICML Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C. Bayan Bruss, Tom Goldstein
2023	J	`jnl`	On the Reliability of Watermarks for Large Language Models. CoRR John Kirchenbauer, Jonas Geiping, Yuxin Wen, Manli Shu, Khalid Saifullah, Kezhi Kong, Kasun Fernando, Aniruddha Saha, Micah Goldblum, Tom Goldstein
2022	A*	`conf`	Robust Optimization as Data Augmentation for Large-scale Graphs. CVPR Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein
2021	A*	`conf`	Data Augmentation for Meta-Learning. ICML Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, Tom Goldstein
2021	A*	`conf`	GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training. NeurIPS Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein
2021	J	`jnl`	GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training. CoRR Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein
2021	A*	`conf`	SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations. AAAI Haozhe Feng, Kezhi Kong, Minghao Chen, Tianye Zhang, Minfeng Zhu, Wei Chen
2021	A*	`conf`	VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization. NeurIPS Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John Dickerson, Furong Huang, Tom Goldstein
2021	J	`jnl`	VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization. CoRR Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John P. Dickerson, Furong Huang, Tom Goldstein
2020	J	`jnl`	Data Augmentation for Meta-Learning. CoRR Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, Tom Goldstein
2020	J	`jnl`	FLAG: Adversarial Data Augmentation for Graph Neural Networks. CoRR Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein
2020	J	`jnl`	SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations. CoRR Haozhe Feng, Kezhi Kong, Minghao Chen, Tianye Zhang, Minfeng Zhu, Wei Chen
2018	—	`conf`	A Visual Analytics Approach for Traffic Flow Prediction Ensembles. PG (Short Papers and Posters) Kezhi Kong, Yuxin Ma, Chentao Ye, Junhua Lu, Xiqun Chen, Wei Zhang, Wei Chen
2018	J	`jnl`	Exploring linear projections for revealing clusters, outliers, and trends in subsets of multi-dimensional datasets. J. Vis. Lang. Comput. Jiazhi Xia, Le Gao, Kezhi Kong, Ying Zhao, Yi Chen, Xiaoyan Kui, Yixiong Liang