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Zhengmian Hu
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2020 – today
- 2024
- [c16]Zhengmian Hu, Tong Zheng, Heng Huang:
A Bayesian Approach to Harnessing the Power of LLMs in Authorship Attribution. EMNLP 2024: 13216-13227 - [c15]Zhengmian Hu, Lichang Chen, Xidong Wu, Yihan Wu, Hongyang Zhang, Heng Huang:
Unbiased Watermark for Large Language Models. ICLR 2024 - [c14]Zhengmian Hu, Heng Huang:
Accelerated Speculative Sampling Based on Tree Monte Carlo. ICML 2024 - [c13]Yihan Wu, Zhengmian Hu, Junfeng Guo, Hongyang Zhang, Heng Huang:
A Resilient and Accessible Distribution-Preserving Watermark for Large Language Models. ICML 2024 - [i16]Yihan Wu, Ruibo Chen, Zhengmian Hu, Yanshuo Chen, Junfeng Guo, Hongyang Zhang, Heng Huang:
Distortion-free Watermarks are not Truly Distortion-free under Watermark Key Collisions. CoRR abs/2406.02603 (2024) - [i15]Chien Van Nguyen, Xuan Shen, Ryan Aponte, Yu Xia, Samyadeep Basu, Zhengmian Hu, Jian Chen, Mihir Parmar, Sasidhar Kunapuli, Joe Barrow, Junda Wu, Ashish Singh, Yu Wang, Jiuxiang Gu, Franck Dernoncourt, Nesreen K. Ahmed, Nedim Lipka, Ruiyi Zhang, Xiang Chen, Tong Yu, Sungchul Kim, Hanieh Deilamsalehy, Namyong Park, Mike Rimer, Zhehao Zhang, Huanrui Yang, Ryan A. Rossi, Thien Huu Nguyen:
A Survey of Small Language Models. CoRR abs/2410.20011 (2024) - [i14]Zhengmian Hu, Heng Huang:
Inevitable Trade-off between Watermark Strength and Speculative Sampling Efficiency for Language Models. CoRR abs/2410.20418 (2024) - [i13]Zhengmian Hu, Tong Zheng, Heng Huang:
A Bayesian Approach to Harnessing the Power of LLMs in Authorship Attribution. CoRR abs/2410.21716 (2024) - [i12]Yanshuo Chen, Zhengmian Hu, Wei Chen, Heng Huang:
Fast and scalable Wasserstein-1 neural optimal transport solver for single-cell perturbation prediction. CoRR abs/2411.00614 (2024) - 2023
- [c12]Xidong Wu, Zhengmian Hu, Heng Huang:
Decentralized Riemannian Algorithm for Nonconvex Minimax Problems. AAAI 2023: 10370-10378 - [c11]Xidong Wu, Feihu Huang, Zhengmian Hu, Heng Huang:
Faster Adaptive Federated Learning. AAAI 2023: 10379-10387 - [c10]Feihu Huang, Xidong Wu, Zhengmian Hu:
AdaGDA: Faster Adaptive Gradient Descent Ascent Methods for Minimax Optimization. AISTATS 2023: 2365-2389 - [c9]Zhengmian Hu, Heng Huang:
Tighter Analysis for ProxSkip. ICML 2023: 13469-13496 - [c8]Zhengmian Hu, Xidong Wu, Heng Huang:
Beyond Lipschitz Smoothness: A Tighter Analysis for Nonconvex Optimization. ICML 2023: 13652-13678 - [c7]Xidong Wu
, Zhengmian Hu
Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining. KDD 2023: 2648-2659 - [c6]Zhengmian Hu, Heng Huang:
Optimization and Bayes: A Trade-off for Overparameterized Neural Networks. NeurIPS 2023 - [c5]Xidong Wu, Jianhui Sun, Zhengmian Hu, Junyi Li, Aidong Zhang, Heng Huang:
Federated Conditional Stochastic Optimization. NeurIPS 2023 - [c4]Xidong Wu, Jianhui Sun, Zhengmian Hu, Aidong Zhang, Heng Huang:
Solving a Class of Non-Convex Minimax Optimization in Federated Learning. NeurIPS 2023 - [i11]Xidong Wu, Zhengmian Hu, Heng Huang:
Decentralized Riemannian Algorithm for Nonconvex Minimax Problems. CoRR abs/2302.03825 (2023) - [i10]Xidong Wu, Zhengmian Hu, Jian Pei, Heng Huang:
Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining. CoRR abs/2308.03035 (2023) - [i9]Xidong Wu, Jianhui Sun, Zhengmian Hu, Junyi Li, Aidong Zhang, Heng Huang:
Federated Conditional Stochastic Optimization. CoRR abs/2310.02524 (2023) - [i8]Xidong Wu, Jianhui Sun, Zhengmian Hu, Aidong Zhang, Heng Huang:
Solving a Class of Non-Convex Minimax Optimization in Federated Learning. CoRR abs/2310.03613 (2023) - [i7]Yihan Wu, Zhengmian Hu, Hongyang Zhang, Heng Huang:
DiPmark: A Stealthy, Efficient and Resilient Watermark for Large Language Models. CoRR abs/2310.07710 (2023) - [i6]Zhengmian Hu, Lichang Chen, Xidong Wu, Yihan Wu, Hongyang Zhang, Heng Huang:
Unbiased Watermark for Large Language Models. CoRR abs/2310.10669 (2023) - [i5]Ruibo Chen, Tianyi Xiong, Yihan Wu, Guodong Liu, Zhengmian Hu, Lichang Chen, Yanshuo Chen, Chenxi Liu, Heng Huang:
GPT-4 Vision on Medical Image Classification - A Case Study on COVID-19 Dataset. CoRR abs/2310.18498 (2023) - [i4]Zhengmian Hu, Gang Wu, Saayan Mitra, Ruiyi Zhang, Tong Sun, Heng Huang, Vishy Swaminathan:
Token-Level Adversarial Prompt Detection Based on Perplexity Measures and Contextual Information. CoRR abs/2311.11509 (2023) - 2022
- [i3]Xidong Wu, Feihu Huang, Zhengmian Hu, Heng Huang:
Faster Adaptive Federated Learning. CoRR abs/2212.00974 (2022) - 2021
- [c3]Huimin Wu, Zhengmian Hu, Bin Gu:
Fast and Scalable Adversarial Training of Kernel SVM via Doubly Stochastic Gradients. AAAI 2021: 10329-10337 - [c2]Zhengmian Hu, Heng Huang:
On the Random Conjugate Kernel and Neural Tangent Kernel. ICML 2021: 4359-4368 - [c1]Zhengmian Hu, Feihu Huang, Heng Huang:
Optimal Underdamped Langevin MCMC Method. NeurIPS 2021: 19363-19374 - [i2]Zhengmian Hu, Feihu Huang, Heng Huang:
A New Framework for Variance-Reduced Hamiltonian Monte Carlo. CoRR abs/2102.04613 (2021) - [i1]Huimin Wu, Zhengmian Hu, Bin Gu:
Fast and Scalable Adversarial Training of Kernel SVM via Doubly Stochastic Gradients. CoRR abs/2107.09937 (2021)
Coauthor Index
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