A Visual Analytics Approach for Traffic Flow Prediction Ensembles. - Kezhi Kong; Yuxin Ma; Chentao Ye; Junhua Lu; Xiqun Chen; Wei Zhang 219; Wei Chen 001 (PG (Short Papers and Posters), 2018)

Exploring linear projections for revealing clusters, outliers, and trends in subsets of multi-dimensional datasets. - Jiazhi Xia; Le Gao; Kezhi Kong; Ying Zhao 001; Yi Chen 007; Xiaoyan Kui; Yixiong Liang (J. Vis. Lang. Comput., 2018)

FLAG: Adversarial Data Augmentation for Graph Neural Networks. - Kezhi Kong; Guohao Li 001; Mucong Ding; Zuxuan Wu; Chen Zhu 001; Bernard Ghanem; Gavin Taylor; Tom Goldstein (CoRR, 2020)

SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations. - Haozhe Feng; Kezhi Kong; Minghao Chen; Tianye Zhang; Minfeng Zhu 001; Wei Chen 001 (AAAI, 2021)

Data Augmentation for Meta-Learning. - Renkun Ni; Micah Goldblum; Amr Sharaf; Kezhi Kong; Tom Goldstein (ICML, 2021)

VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization. - Mucong Ding; Kezhi Kong; Jingling Li; Chen Zhu 001; John Dickerson 001; Furong Huang; Tom Goldstein (NeurIPS, 2021)

GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training. - Chen Zhu 001; Renkun Ni; Zheng Xu 002; Kezhi Kong; W. Ronny Huang; Tom Goldstein (NeurIPS, 2021)

Robust Optimization as Data Augmentation for Large-scale Graphs. - Kezhi Kong; Guohao Li 001; Mucong Ding; Zuxuan Wu; Chen Zhu 001; Bernard Ghanem; Gavin Taylor; Tom Goldstein (CVPR, 2022)

GOAT: A Global Transformer on Large-scale Graphs. - Kezhi Kong; Jiuhai Chen; John Kirchenbauer; Renkun Ni; C. Bayan Bruss; Tom Goldstein (ICML, 2023)

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)

OpenTab: Advancing Large Language Models as Open-domain Table Reasoners. - Kezhi Kong; Jiani Zhang 003; Zhengyuan Shen; Balasubramaniam Srinivasan; Chuan Lei; Christos Faloutsos; Huzefa Rangwala; George Karypis (ICLR, 2024)

Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase Pretraining. - Steven Feng; Shrimai Prabhumoye; Kezhi Kong; Dan Su 003; Mostofa Patwary; Mohammad Shoeybi; Bryan Catanzaro (CoRR, 2024)

Towards Generalized and Scalable Machine Learning on Structured Data. - Kezhi Kong (2024)

Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset. - Dan Su 003; Kezhi Kong; Ying Lin; Joseph Jennings; Brandon Norick; Markus Kliegl; Mostofa Patwary; Mohammad Shoeybi; Bryan Catanzaro (ACL (1), 2025)

Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models. - Aaron Blakeman; Aarti Basant; Abhinav Khattar; Adithya Renduchintala; Akhiad Bercovich; Aleksander Ficek; Alexis Bjorlin; Ali Taghibakhshi; Amala Sanjay Deshmukh; Ameya Sunil Mahabaleshwarkar; Andrew Tao; Anna Shors; et al. (CoRR, 2025)

Llama-Nemotron: Efficient Reasoning Models. - Akhiad Bercovich; Itay Levy; Izik Golan; Mohammad Dabbah; Ran El-Yaniv; Omri Puny; Ido Galil; Zach Moshe; Tomer Ronen; Najeeb Nabwani; Ido Shahaf; Oren Tropp; et al. (CoRR, 2025)

NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model. - Aarti Basant; Abhijit Khairnar; Abhijit Paithankar; Abhinav Khattar; Adithya Renduchintala; Aditya Malte; Akhiad Bercovich; Akshay Hazare; Alejandra Rico; Aleksander Ficek; Alex Kondratenko; Alex Shaposhnikov; et al. (CoRR, 2025)