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ORCIDHanze Dong
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2020 – today
- 2024
[c11]Xunpeng Huang, Difan Zou, Hanze Dong, Yi-An Ma, Tong Zhang:
Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo. COLT 2024: 2438-2493- [c10]Xunpeng Huang, Hanze Dong, Yifan Hao, Yian Ma, Tong Zhang:
Reverse Diffusion Monte Carlo. ICLR 2024 - [c9]Yong Lin, Lu Tan, Yifan Hao, Honam Wong, Hanze Dong, Weizhong Zhang, Yujiu Yang, Tong Zhang:
Spurious Feature Diversification Improves Out-of-distribution Generalization. ICLR 2024 - [c8]Wei Xiong, Hanze Dong, Chenlu Ye, Ziqi Wang, Han Zhong, Heng Ji, Nan Jiang, Tong Zhang:
Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-constraint. ICML 2024 - [c7]Xunpeng Huang, Difan Zou, Hanze Dong, Yian Ma, Tong Zhang:
Faster Sampling via Stochastic Gradient Proximal Sampler. ICML 2024 - [c6]Shizhe Diao, Rui Pan, Hanze Dong, Kashun Shum, Jipeng Zhang, Wei Xiong, Tong Zhang:
LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. NAACL (Demonstrations) 2024: 116-127 - [i26]Renjie Pi, Tianyang Han, Yueqi Xie, Rui Pan, Qing Lian, Hanze Dong, Jipeng Zhang, Tong Zhang:
MLLM-Protector: Ensuring MLLM's Safety without Hurting Performance. CoRR abs/2401.02906 (2024) - [i25]Xunpeng Huang, Difan Zou, Hanze Dong, Yian Ma, Tong Zhang:
Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo. CoRR abs/2401.06325 (2024) - [i24]Xunpeng Huang, Hanze Dong, Difan Zou, Tong Zhang:
An Improved Analysis of Langevin Algorithms with Prior Diffusion for Non-Log-Concave Sampling. CoRR abs/2403.06183 (2024) - [i23]Hanze Dong, Wei Xiong, Bo Pang, Haoxiang Wang, Han Zhao, Yingbo Zhou, Nan Jiang, Doyen Sahoo, Caiming Xiong, Tong Zhang:
RLHF Workflow: From Reward Modeling to Online RLHF. CoRR abs/2405.07863 (2024) - [i22]Xunpeng Huang, Difan Zou, Hanze Dong, Yi Zhang, Yi-An Ma, Tong Zhang:
Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference. CoRR abs/2405.16387 (2024) - [i21]Xunpeng Huang, Difan Zou, Yi-An Ma, Hanze Dong, Tong Zhang:
Faster Sampling via Stochastic Gradient Proximal Sampler. CoRR abs/2405.16734 (2024) - [i20]Yuhui Xu, Zhanming Jie, Hanze Dong, Lei Wang, Xudong Lu, Aojun Zhou, Amrita Saha, Caiming Xiong, Doyen Sahoo:
ThinK: Thinner Key Cache by Query-Driven Pruning. CoRR abs/2407.21018 (2024) - [i19]KaShun Shum, Minrui Xu, Jianshu Zhang, Zixin Chen, Shizhe Diao, Hanze Dong, Jipeng Zhang, Muhammad Omer Raza:
FIRST: Teach A Reliable Large Language Model Through Efficient Trustworthy Distillation. CoRR abs/2408.12168 (2024) - 2023
- [j6]Hanze Dong, Wei Xiong, Deepanshu Goyal, Yihan Zhang, Winnie Chow, Rui Pan, Shizhe Diao, Jipeng Zhang, Kashun Shum, Tong Zhang:
RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment. Trans. Mach. Learn. Res. 2023 (2023) - [c5]Xun Qian, Hanze Dong, Tong Zhang, Peter Richtárik:
Catalyst Acceleration of Error Compensated Methods Leads to Better Communication Complexity. AISTATS 2023: 615-649 - [c4]Renjie Pi, Jiahui Gao, Shizhe Diao, Rui Pan, Hanze Dong, Jipeng Zhang, Lewei Yao, Jianhua Han, Hang Xu, Lingpeng Kong, Tong Zhang:
DetGPT: Detect What You Need via Reasoning. EMNLP 2023: 14172-14189 - [c3]Hanze Dong, Xi Wang, Yong Lin, Tong Zhang:
Particle-based Variational Inference with Preconditioned Functional Gradient Flow. ICLR 2023 - [i18]Yanwei Fu
, Xiaomei Wang, Hanze Dong, Yu-Gang Jiang, Meng Wang, Xiangyang Xue, Leonid Sigal:
Vocabulary-informed Zero-shot and Open-set Learning. CoRR abs/2301.00998 (2023) - [i17]Shihong Ding, Hanze Dong, Cong Fang, Zhouchen Lin, Tong Zhang:
Provable Particle-based Primal-Dual Algorithm for Mixed Nash Equilibrium. CoRR abs/2303.00970 (2023) - [i16]Hanze Dong, Wei Xiong, Deepanshu Goyal, Rui Pan, Shizhe Diao, Jipeng Zhang, Kashun Shum, Tong Zhang:
RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment. CoRR abs/2304.06767 (2023) - [i15]Renjie Pi, Jiahui Gao, Shizhe Diao, Rui Pan, Hanze Dong, Jipeng Zhang, Lewei Yao, Jianhua Han, Hang Xu, Lingpeng Kong, Tong Zhang:
DetGPT: Detect What You Need via Reasoning. CoRR abs/2305.14167 (2023) - [i14]Shizhe Diao, Rui Pan, Hanze Dong, Kashun Shum, Jipeng Zhang, Wei Xiong, Tong Zhang:
LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. CoRR abs/2306.12420 (2023) - [i13]Xunpeng Huang, Hanze Dong, Yifan Hao, Yian Ma, Tong Zhang:
Monte Carlo Sampling without Isoperimetry: A Reverse Diffusion Approach. CoRR abs/2307.02037 (2023) - [i12]Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, Hanze Dong, Renjie Pi, Han Zhao, Nan Jiang, Yuan Yao, Tong Zhang:
Mitigating the Alignment Tax of RLHF. CoRR abs/2309.06256 (2023) - [i11]Yong Lin, Lu Tan, Yifan Hao, Honam Wong, Hanze Dong, Weizhong Zhang, Yujiu Yang, Tong Zhang:
Spurious Feature Diversification Improves Out-of-distribution Generalization. CoRR abs/2309.17230 (2023) - [i10]Wei Xiong, Hanze Dong, Chenlu Ye, Han Zhong, Nan Jiang, Tong Zhang:
Gibbs Sampling from Human Feedback: A Provable KL- constrained Framework for RLHF. CoRR abs/2312.11456 (2023) - 2022
- [j5]Hanze Dong, Yanwei Fu, Sung Ju Hwang, Leonid Sigal, Xiangyang Xue:
Learning the Compositional Domains for Generalized Zero-shot Learning. Comput. Vis. Image Underst. 221: 103454 (2022) - [j4]Xinwei Shen, Furui Liu, Hanze Dong, Qing Lian, Zhitang Chen, Tong Zhang:
Weakly Supervised Disentangled Generative Causal Representation Learning. J. Mach. Learn. Res. 23: 241:1-241:55 (2022) - [c2]Yong Lin, Hanze Dong, Hao Wang, Tong Zhang:
Bayesian Invariant Risk Minimization. CVPR 2022: 16000-16009 - [c1]Songtao Liu, Rex Ying, Hanze Dong, Lanqing Li, Tingyang Xu, Yu Rong, Peilin Zhao, Junzhou Huang, Dinghao Wu:
Local Augmentation for Graph Neural Networks. ICML 2022: 14054-14072 - [i9]Songtao Liu, Rex Ying, Hanze Dong, Lu Lin, Jinghui Chen, Dinghao Wu:
How Powerful is Implicit Denoising in Graph Neural Networks. CoRR abs/2209.14514 (2022) - [i8]Hanze Dong, Shizhe Diao, Weizhong Zhang, Tong Zhang:
Normalizing Flow with Variational Latent Representation. CoRR abs/2211.11638 (2022) - [i7]Hanze Dong, Xi Wang, Yong Lin, Tong Zhang:
Particle-based Variational Inference with Preconditioned Functional Gradient Flow. CoRR abs/2211.13954 (2022) - 2021
- [j3]Cong Fang, Hanze Dong, Tong Zhang:
Mathematical Models of Overparameterized Neural Networks. Proc. IEEE 109(5): 683-703 (2021) - [i6]Songtao Liu, Hanze Dong, Lanqing Li, Tingyang Xu, Yu Rong, Peilin Zhao, Junzhou Huang, Dinghao Wu:
Local Augmentation for Graph Neural Networks. CoRR abs/2109.03856 (2021) - 2020
- [j2]Hanze Dong, Zhenfeng Sun, Yanwei Fu, Shi Zhong, Zhengjun Zhang, Yu-Gang Jiang:
Extreme vocabulary learning. Frontiers Comput. Sci. 14(6): 146315 (2020) - [j1]Yanwei Fu, Xiaomei Wang, Hanze Dong, Yu-Gang Jiang, Meng Wang, Xiangyang Xue, Leonid Sigal:
Vocabulary-Informed Zero-Shot and Open-Set Learning. IEEE Trans. Pattern Anal. Mach. Intell. 42(12): 3136-3152 (2020) - [i5]Xinwei Shen, Furui Liu, Hanze Dong, Qing Lian, Zhitang Chen, Tong Zhang:
Disentangled Generative Causal Representation Learning. CoRR abs/2010.02637 (2020) - [i4]Cong Fang, Hanze Dong, Tong Zhang:
Mathematical Models of Overparameterized Neural Networks. CoRR abs/2012.13982 (2020)
2010 – 2019
- 2019
- [i3]Cong Fang, Hanze Dong, Tong Zhang:
Over Parameterized Two-level Neural Networks Can Learn Near Optimal Feature Representations. CoRR abs/1910.11508 (2019) - [i2]Songtao Liu, Lingwei Chen, Hanze Dong, Zihao Wang, Dinghao Wu, Zengfeng Huang:
Higher-order Weighted Graph Convolutional Networks. CoRR abs/1911.04129 (2019) - 2018
- [i1]Hanze Dong, Yanwei Fu, Leonid Sigal, Sung Ju Hwang, Yu-Gang Jiang, Xiangyang Xue:
Learning to Separate Domains in Generalized Zero-Shot and Open Set Learning: a probabilistic perspective. CoRR abs/1810.07368 (2018)
Coauthor Index
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