Kezhi Kong

PhD student, University of Maryland, College Park

Joined

August 2019

Names

Kezhi Kong (Preferred)

Emails

****@cs.umd.edu (Confirmed)

****@gmail.com (Confirmed)

Personal Links

Homepage

DBLP

Career & Education History

PhD student

University of Maryland, College Park (umd.edu)

2019 – Present

Undergrad student

Zhejiang University (zju.edu.cn)

2015 – 2019

Advisors, Relations & Conflicts

PhD Advisor

Tom Goldstein

2019 – Present

Expertise

Diffusion Model

Object Detection

2022 – Present

Language Model

2022 – Present

GNNs

Graph Neural Networks

Machine Learning

2019 – Present

Semi-supervised Learning

VAE

2018 – 2019

Publications

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 poster
- Readers: Everyone
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 poster
- Readers: Everyone
GOAT: A Global Transformer on Large-scale Graphs
Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C. Bayan Bruss, Tom Goldstein
- ICML 2023 Poster
- Readers: Everyone
GOAT: A Global Transformer on Large-scale Graphs
Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C. Bayan Bruss, Tom Goldstein
- 22 Sept 2022 (modified: 13 Feb 2023)
- ICLR 2023 Conference Withdrawn Submission
- Readers: Everyone
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
- 15 May 2022
- OpenReview Archive Direct Upload
- Readers: Everyone
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
- 2022 (modified: 18 Nov 2022)
- CVPR 2022
- Readers: Everyone
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
- Published: 02 Dec 2021, Last Modified: 05 May 2023
- NeurIPS 2021 Workshop DistShift Spotlight
- Readers: Everyone
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
- 29 Sept 2021 (modified: 13 Feb 2023)
- ICLR 2022 Conference Withdrawn Submission
- Readers: Everyone
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization
Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John P Dickerson, Furong Huang, Tom Goldstein
- Published: 09 Nov 2021, Last Modified: 05 May 2023
- NeurIPS 2021 Poster
- Readers: Everyone
GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training
Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W Ronny Huang, Tom Goldstein
- Published: 09 Nov 2021, Last Modified: 26 May 2025
- NeurIPS 2021 Poster
- Readers: Everyone

View all 21 publications

Co-Authors

View all 40 co-authors