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2020 – today
- 2024
[c9]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- [c8]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 - [i7]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 abs/2402.14361 (2024) - 2023
- [c7]Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Renkun Ni, C. Bayan Bruss, Tom Goldstein:
GOAT: A Global Transformer on Large-scale Graphs. ICML 2023: 17375-17390 - [i6]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 abs/2306.04634 (2023) - 2022
- [c6]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: 60-69 - 2021
- [c5]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: 7413-7421 - [c4]Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, Tom Goldstein:
Data Augmentation for Meta-Learning. ICML 2021: 8152-8161 - [c3]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: 6733-6746 - [c2]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: 16410-16422 - [i5]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 abs/2102.08098 (2021) - [i4]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 abs/2110.14363 (2021) - 2020
- [i3]Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, Tom Goldstein:
Data Augmentation for Meta-Learning. CoRR abs/2010.07092 (2020) - [i2]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 abs/2010.09891 (2020) - [i1]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 abs/2011.10684 (2020)
2010 – 2019
- 2018
- [j1]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: 52-60 (2018) - [c1]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: 61-64
Coauthor Index
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