Kezhi Kong

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 18

Top Topics

Vector Quantization

Discriminative Learning Framework

Top Venues

Publications

Kezhi Kong, Jiani Zhang, Zhengyuan Shen, Balasubramaniam Srinivasan, Chuan Lei, Christos Faloutsos, Huzefa Rangwala, George Karypis
OpenTab: Advancing Large Language Models as Open-domain Table Reasoners. CoRR (2024)
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Manli Shu, Khalid Saifullah, Kezhi Kong, Kasun Fernando, Aniruddha Saha, Micah Goldblum, Tom Goldstein
On the Reliability of Watermarks for Large Language Models. ICLR (2024)
Kezhi Kong, Jiani Zhang, Zhengyuan Shen, Balasubramaniam Srinivasan, Chuan Lei, Christos Faloutsos, Huzefa Rangwala, George Karypis
OpenTab: Advancing Large Language Models as Open-domain Table Reasoners. ICLR (2024)
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Manli Shu, Khalid Saifullah, Kezhi Kong, Kasun Fernando, Aniruddha Saha, Micah Goldblum, Tom Goldstein
On the Reliability of Watermarks for Large Language Models. CoRR (2023)
Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C. Bayan Bruss, Tom Goldstein
GOAT: A Global Transformer on Large-scale Graphs. ICML (2023)
Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein
Robust Optimization as Data Augmentation for Large-scale Graphs. CVPR (2022)
Chen Chen, Kezhi Kong, Peihong Yu, Juan Luque, Tom Goldstein, Furong Huang
Insta-RS: Instance-wise Randomized Smoothing for Improved Robustness and Accuracy. CoRR (2021)
Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John P. Dickerson, Furong Huang, Tom Goldstein
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization. CoRR (2021)
Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John Dickerson, Furong Huang, Tom Goldstein
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization. NeurIPS (2021)
Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, Tom Goldstein
Data Augmentation for Meta-Learning. ICML (2021)
Haozhe Feng, Kezhi Kong, Minghao Chen, Tianye Zhang, Minfeng Zhu, Wei Chen
SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations. AAAI (2021)
Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein
GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training. CoRR (2021)
Chen Zhu, Renkun Ni, Zheng Xu, Kezhi Kong, W. Ronny Huang, Tom Goldstein
GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training. NeurIPS (2021)
Haozhe Feng, Kezhi Kong, Minghao Chen, Tianye Zhang, Minfeng Zhu, Wei Chen
SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations. CoRR (2020)
Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein
FLAG: Adversarial Data Augmentation for Graph Neural Networks. CoRR (2020)
Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, Tom Goldstein
Data Augmentation for Meta-Learning. CoRR (2020)
Jiazhi Xia, Le Gao, Kezhi Kong, Ying Zhao, Yi Chen, Xiaoyan Kui, Yixiong Liang
Exploring linear projections for revealing clusters, outliers, and trends in subsets of multi-dimensional datasets. J. Vis. Lang. Comput. 48 (2018)
Kezhi Kong, Yuxin Ma, Chentao Ye, Junhua Lu, Xiqun Chen, Wei Zhang, Wei Chen
A Visual Analytics Approach for Traffic Flow Prediction Ensembles. PG (Short Papers and Posters) (2018)