Hanze Dong
About
Hanze Dong is a research scientist at Salesforce Research. Currently, he also serves as managing editor of the Journal of Machine Learning Research (JMLR) . He earned his PhD in Mathematics from the Hong Kong University of Science and Technology (HKUST), where he was advised by Tong Zhang. He was also a visiting scholar at UC San Diego, collaborating with Yi-An Ma. Prior to his doctoral studies, he completed his Bachelor of Science in Mathematics at Fudan University under the supervision of Yanwei Fu.
His current research focuses on the post-training and alignment of foundation models, generative modeling, and Monte Carlo sampling. He also has some experience in designing robust and efficient ML algorithms as well as ML theory.
News
[2024-10] PAPAL has been accepted to JMLR.[2024-09] Two papers have been accepted to NeurIPS 2024.
[2024-09] Three papers have been accepted to EMNLP 2024 Main Conference.
[2024-09] RLHF Workflow has been accepted at TMLR.
[2024-07] Reverse Transition Kernel is selected as the best paper at ICML 2024 Workshop SPIGM.
[2024-07] Attending ICML 2024 at Vienna, Austria
[2024-06] LMFlow has won Best Demo Award at NAACL 2024!
[2024-05] One paper has been accepted to COLT 2024, which provides the first sampling algorithm that support general non-log-Sobolev distribution with quasi-polynomial computation complexity.
[2024-05] Two papers have been accepted to ICML 2024, including Gibbs sampling from human feedback (GSHF) and stochastic proximal sampler.
[2024-03] Excited to share that LMFlow paper was accepted by NAACL 2024 Demo Track.
[2024-01] Two papers have been accepted to ICLR 2024, including rdMC and Spurious feature diversification.
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[2023-12] The defense of the PhD thesis was successfully completed on November 30th, 2023.
[2023-11] Excited to share that RAFT has been accepted to TMLR. [Link].
[2023-07] Attending ICML 2023 at Honolulu, Hawaiʻi, USA.
[2023-05] Received HKUST RedBird Academic Excellence Award.
[2023-05] Attending ICLR 2023 at Kigali, Rwanda.
[2023-03] We're thrilled to release our project, LMFlow! This framework streamlines the development of LLMs, including fine-tuning, inference, and RLHF, in a more cost-effective and effortless manner. Our aspiration is that LMFlow will incite a broader range of imaginative applications of LLMs and cultivate a larger community of LLM aficionados!
[2023-11] Excited to share that RAFT has been accepted to TMLR. [Link].
[2023-07] Attending ICML 2023 at Honolulu, Hawaiʻi, USA.
[2023-05] Received HKUST RedBird Academic Excellence Award.
[2023-05] Attending ICLR 2023 at Kigali, Rwanda.
[2023-03] We're thrilled to release our project, LMFlow! This framework streamlines the development of LLMs, including fine-tuning, inference, and RLHF, in a more cost-effective and effortless manner. Our aspiration is that LMFlow will incite a broader range of imaginative applications of LLMs and cultivate a larger community of LLM aficionados!
Research
Alignment of Foundation Models
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Online Iterative Reinforcement Learning from Human Feedback with General Preference Model
Chenlu Ye*, Wei Xiong*, Yuheng Zhang*, Hanze Dong*, Nan Jiang, Tong Zhang;
Annual Conference on Neural Information Processing Systems (NeurIPS), 2024. [Paper] -
RLHF workflow: From Reward Modeling to online RLHF
Hanze Dong*, Wei Xiong*, Bo Pang*, Haoxiang Wang*, Han Zhao, Yingbo Zhou, Nan Jiang, Doyen Sahoo, Caiming Xiong, Tong Zhang;
Transactions on Machine Learning Research (TMLR), 2024. [Paper] -
Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-constraint
Wei Xiong*, Hanze Dong*, Chenlu Ye*, Ziqi Wang, Han Zhong, Heng Ji, Nan Jiang, Tong Zhang;
International Conference on Machine Learning (ICML), 2024.
ICLR 2024 Workshop on Mathematical and Empirical Understanding of Foundation Models (Oral). [Paper] -
LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models.
Shizhe Diao*, Rui Pan*, Hanze Dong*, Kashun Shum, Jipeng Zhang, Wei Xiong, Tong Zhang;
Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) -- System Demonstration Track, 2024. (Best Demo Award) [Paper] [Github] -
Raft: Reward ranked finetuning for generative foundation model alignment
Hanze Dong*, Wei Xiong*, Deepanshu Goyal, Yihan Zhang, Winnie Chow, Rui Pan, Shizhe Diao, Jipeng Zhang, Kashun Shum, Tong Zhang;
Transactions on Machine Learning Research (TMLR), 2023. [Paper] -
DetGPT: Detect What You Need via Reasoning
Renjie Pi, Jiahui Gao, Shizhe Diao, Rui Pan, Hanze Dong, Jipeng Zhang, Lewei Yao, Jianhua Han, Hang Xu, Lingpeng Kong, Tong Zhang;
Empirical Methods in Natural Language Processing (EMNLP), 2023. [Paper]
Generative Modeling and Monte Carlo Sampling
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Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference
Xunpeng Huang, Difan Zou, Hanze Dong, Yi Zhang, Yian Ma, Tong Zhang;
Annual Conference on Neural Information Processing Systems (NeurIPS), 2024. (Spotlight) [Paper] -
Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo
Xunpeng Huang, Difan Zou, Hanze Dong, Yian Ma, Tong Zhang;
Annual Conference on Learning Theory (COLT), 2024. [Paper] -
Faster Sampling via Stochastic Gradient Proximal Sampler
Xunpeng Huang, Difan Zou, Hanze Dong, Yian Ma, Tong Zhang;
International Conference on Machine Learning (ICML), 2024. [Paper] -
Reverse Diffusion Monte Carlo.
Xunpeng Huang*, Hanze Dong*, Yifan Hao, Yian Ma, Tong Zhang;
International Conference on Learning Representations (ICLR), 2024. [Paper] -
Particle-based Variational Inference with Preconditioned Functional Gradient Flow
Hanze Dong, Xi Wang, Yong Lin, Tong Zhang;
International Conference on Learning Representations (ICLR), 2023. [Paper] -
Weakly Supervised Disentangled Generative Causal Representation Learning
Xinwei Shen, Furui Liu, Hanze Dong, Qing Lian, Zhitang Chen, Tong Zhang;
Journal of Machine Learning Research (JMLR), 2022. [Paper] -
Normalizing Flow with Variational Latent Representation
Hanze Dong*, Shizhe Diao*, Weizhong Zhang, Tong Zhang;
arXiv preprint arXiv:2211.11638, 2022. [Paper]
Robust Machine Learning
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Spurious feature diversification improves out-of-distribution generalization
Yong Lin, Lu Tan, Yifan Hao, Honam Wong, Hanze Dong, Weizhong Zhang, Yujiu Yang, Tong Zhang;
International Conference on Learning Representations (ICLR), 2024. -
Bayesian Invariant Risk Minimization
Yong Lin*, Hanze Dong*, Hao Wang, Tong Zhang;
Conference on Computer Vision and Pattern Recognition (CVPR), 2022. (Oral) [Paper] -
Local Augmentation for Graph Neural Networks
Songtao Liu, Rex Ying, Hanze Dong, Lanqing Li, Tingyang Xu, Yu Rong, Peilin Zhao, Junzhou Huang, Dinghao Wu;
International Conference on Machine Learning (ICML), 2022. [Paper] -
Learning the Compositional Spaces for Generalized Zero-shot Learning
Hanze Dong, Yanwei Fu, Leonid Sigal, Sung Ju Hwang, Xiangyang Xue;
Computer Vision and Image Understanding (CVIU), 2022. [Paper] -
Vocabulary-informed Zero-shot and Open-set Learning
Yanwei Fu, Xiaomei Wang, Hanze Dong, Yu-Gang Jiang, Meng Wang, Xiangyang Xue, Leonid Sigal;
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019. [Paper] -
Extreme vocabulary learning
Hanze Dong, Zhenfeng Sun, Yanwei Fu, Shi Zhong, Zhengjun Zhang, Yu-Gang Jiang;
Frontiers of Computer Science, 2019. [Paper]
Optimization and Efficient Machine Learning
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Catalyst Acceleration of Error Compensated Methods Leads to Better Communication Complexity
Xun Qian, Hanze Dong, Tong Zhang, Peter Richtárik;
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023. [Paper] -
Error Compensated Loopless SVRG for Distributed Optimization
Xun Qian, Hanze Dong, Peter Richtárik, Tong Zhang;
OPT2020: 12th Annual Workshop on Optimization for Machine Learning, 2020. [Paper] -
Error Compensated Proximal SGD and RDA
Xun Qian, Hanze Dong, Peter Richtárik, Tong Zhang;
OPT2020: 12th Annual Workshop on Optimization for Machine Learning, 2020. [Paper]
Machine Learning Theory
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Mathematical models of Overparameterized Neural Networks
Cong Fang, Hanze Dong, Tong Zhang;
Proceedings of the IEEE (PIEEE), 2021. [Paper] -
Provable Particle-based Primal-Dual Algorithm for Mixed Nash Equilibrium
Shihong Ding*, Hanze Dong*, Cong Fang, Zhouchen Lin, Tong Zhang;
Journal of Machine Learning Research (JMLR), 2024. [Paper]
Academic Services
Teaching
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2022-2023 Fall --
MATH 2011: Introduction to Multivariable Calculus.
2022-2023 Fall -- MATH 6450J/COMP 6211E: Optimization for Machine Learning.
2021-2022 Spring -- MATH 2011: Introduction to Multivariable Calculus.
2021-2022 Fall -- MATH 1023: Honor Calculus.
2020-2021 Spring -- MATH 1014: Calculus II.
2020-2021 Fall -- MATH 1013: Calculus IB.
2019-2020 Spring -- MATH 1014: Calculus II.
2019-2020 Spring -- MATH 2411: Applied Statistics.
Awards
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2024 --
Best Paper Award of ICML 2024 Workshop on Structured Probabilistic Inference & Generative Modeling.
2024 -- NAACL Best Demo Paper Award.
2022/2023 -- RedBird PhD Award, The Hong Kong University of Science and Technology.
2020/2021/2022 -- Best TA Award, The Hong Kong University of Science and Technology.
2019 -- Outstanding Graduates, Fudan University.
2016 -- The First Price, China Undergraduate Physics Tournament
2015 -- Outstanding Freshmen, Fudan University
2014 -- Bronze Medal and The First Price, Chinese Physics Olympiad.
Contact Me
A at B dot com
A = hendrydong
B = gmail