Kezhi Kong

According to our database, Kezhi Kong authored at least 14 papers between 2018 and 2024.

Collaborative distances:

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

2024

Towards Generalized and Scalable Machine Learning on Structured Data.

Kezhi Kong

PhD thesis, 2024

Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase Pretraining.

Steven Feng, Shrimai Prabhumoye, Kezhi Kong, Dan Su, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro

CoRR, 2024

Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset.

Dan Su, Kezhi Kong, Ying Lin, Joseph Jennings, Brandon Norick, Markus Kliegl, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro

CoRR, 2024

OpenTab: Advancing Large Language Models as Open-domain Table Reasoners.

Kezhi Kong, Jiani Zhang, Zhengyuan Shen, Balasubramaniam Srinivasan, Chuan Lei, Christos Faloutsos, Huzefa Rangwala, George Karypis

Proceedings of the Twelfth International Conference on Learning Representations, 2024

On the Reliability of Watermarks for Large Language Models.

John Kirchenbauer, Jonas Geiping, Yuxin Wen, Manli Shu, Khalid Saifullah, Kezhi Kong, Kasun Fernando, Aniruddha Saha, Micah Goldblum, Tom Goldstein

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

GOAT: A Global Transformer on Large-scale Graphs.

Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C. Bayan Bruss, Tom Goldstein

Proceedings of the International Conference on Machine Learning, 2023

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

GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training.

Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein

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

Data Augmentation for Meta-Learning.

Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, Tom Goldstein

Proceedings of the 38th International Conference on Machine Learning, 2021

SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations.

Haozhe Feng, Kezhi Kong, Minghao Chen, Tianye Zhang, Minfeng Zhu, Wei Chen

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 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

2018

Exploring linear projections for revealing clusters, outliers, and trends in subsets of multi-dimensional datasets.

Jiazhi Xia, Le Gao, Kezhi Kong, Ying Zhao, Yi Chen, Xiaoyan Kui, Yixiong Liang

J. Vis. Lang. Comput., 2018

A Visual Analytics Approach for Traffic Flow Prediction Ensembles.

Kezhi Kong, Yuxin Ma, Chentao Ye, Junhua Lu, Xiqun Chen, Wei Zhang, Wei Chen

Proceedings of the 26th Pacific Conference on Computer Graphics and Applications, 2018