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2020 – today
- 2024
[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 - [i12]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) - 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, Jian Pei, Heng Huang:
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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