Kezhi Kong

I am a Research Scientist at NVIDIA, ADLR team.

I earned my PhD degree from the Computer Science Department, University of Maryland, College Park, where I was advised by Prof. Tom Goldstein. I obtained my bachelor's degree from Zhejiang University, supervised by Prof. Wei Chen. I can be reached by my-last-name@terpmail.umd.edu.

My name in Chinese: 孔柯智

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Research

I do research in Machine Learning. My recent works focus on Large Language Models, Graph Representation Learning, and Trustworthy ML. Below are my selected publications.

	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. ICLR, 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. ICLR, 2024
	GOAT: A Global Transformer on Large-scale Graphs Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C. Bayan Bruss, Tom Goldstein. ICML, 2023
	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. CVPR, 2022
	VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization Kezhi Kong, Mucong Ding, Jingling Li, Chen Zhu, John P Dickerson, Furong Huang, Tom Goldstein. NeurIPS, 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. DistShift Workshop @ NeurIPS (Spotlight), 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. NeurIPS, 2021
	Data Augmentation for Meta-Learning Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, Tom Goldstein. ICML, 2021
	SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations Hao-Zhe Feng, Kezhi Kong, Minghao Chen, Tianye Zhang, Minfeng Zhu, Wei Chen. AAAI, 2021

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