Learning to Separate Domains in Generalized Zero-Shot and Open Set Learning: a probabilistic perspective. - Hanze Dong; Yanwei Fu 001; Leonid Sigal; Sung Ju Hwang; Yu-Gang Jiang 001; Xiangyang Xue 001 (CoRR, 2018)

Over Parameterized Two-level Neural Networks Can Learn Near Optimal Feature Representations. - Cong Fang 001; Hanze Dong; Tong Zhang 001 (CoRR, 2019)

Higher-order Weighted Graph Convolutional Networks. - Songtao Liu; Lingwei Chen; Hanze Dong; Zihao Wang; Dinghao Wu; Zengfeng Huang (CoRR, 2019)

Disentangled Generative Causal Representation Learning. - Xinwei Shen 002; Furui Liu; Hanze Dong; Qing Lian; Zhitang Chen; Tong Zhang 001 (CoRR, 2020)

Extreme vocabulary learning. - Hanze Dong; Zhenfeng Sun; Yanwei Fu 001; Shi Zhong; Zhengjun Zhang; Yu-Gang Jiang 001 (Frontiers Comput. Sci., 2020)

Vocabulary-Informed Zero-Shot and Open-Set Learning. - Yanwei Fu 001; Xiaomei Wang; Hanze Dong; Yu-Gang Jiang 001; Meng Wang 001; Xiangyang Xue 001; Leonid Sigal (IEEE Trans. Pattern Anal. Mach. Intell., 2020)

Local Augmentation for Graph Neural Networks. - Songtao Liu; Hanze Dong; Lanqing Li; Tingyang Xu; Yu Rong 001; Peilin Zhao; Junzhou Huang; Dinghao Wu (CoRR, 2021)

Mathematical Models of Overparameterized Neural Networks. - Cong Fang 001; Hanze Dong; Tong Zhang 001 (Proc. IEEE, 2021)

Bayesian Invariant Risk Minimization. - Yong Lin; Hanze Dong; Hao Wang 014; Tong Zhang 001 (CVPR, 2022)

Local Augmentation for Graph Neural Networks. - Songtao Liu; Rex Ying; Hanze Dong; Lanqing Li; Tingyang Xu; Yu Rong 001; Peilin Zhao; Junzhou Huang; Dinghao Wu (ICML, 2022)

How Powerful is Implicit Denoising in Graph Neural Networks. - Songtao Liu; Rex Ying; Hanze Dong; Lu Lin 001; Jinghui Chen; Dinghao Wu (CoRR, 2022)

Normalizing Flow with Variational Latent Representation. - Hanze Dong; Shizhe Diao; Weizhong Zhang; Tong Zhang 001 (CoRR, 2022)

Learning the Compositional Domains for Generalized Zero-shot Learning. - Hanze Dong; Yanwei Fu 001; Sung Ju Hwang; Leonid Sigal; Xiangyang Xue 001 (Comput. Vis. Image Underst., 2022)

Weakly Supervised Disentangled Generative Causal Representation Learning. - Xinwei Shen 002; Furui Liu; Hanze Dong; Qing Lian; Zhitang Chen; Tong Zhang 001 (J. Mach. Learn. Res., 2022)

Catalyst Acceleration of Error Compensated Methods Leads to Better Communication Complexity. - Xun Qian; Hanze Dong; Tong Zhang 001; Peter Richtárik (AISTATS, 2023)

DetGPT: Detect What You Need via Reasoning. - Renjie Pi; Jiahui Gao; Shizhe Diao; Rui Pan 002; Hanze Dong; Jipeng Zhang; Lewei Yao; Jianhua Han; Hang Xu 004; Lingpeng Kong; Tong Zhang 001 (EMNLP, 2023)

Particle-based Variational Inference with Preconditioned Functional Gradient Flow. - Hanze Dong; Xi Wang; Yong Lin; Tong Zhang 001 (ICLR, 2023)

Provable Particle-based Primal-Dual Algorithm for Mixed Nash Equilibrium. - Shihong Ding; Hanze Dong; Cong Fang 001; Zhouchen Lin; Tong Zhang 001 (CoRR, 2023)

RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment. - Hanze Dong; Wei Xiong 015; Deepanshu Goyal; Rui Pan 002; Shizhe Diao; Jipeng Zhang; Kashun Shum; Tong Zhang 001 (CoRR, 2023)

Monte Carlo Sampling without Isoperimetry: A Reverse Diffusion Approach. - Xunpeng Huang; Hanze Dong; Yifan Hao 002; Yian Ma; Tong Zhang 001 (CoRR, 2023)

Mitigating the Alignment Tax of RLHF. - Yong Lin; Hangyu Lin; Wei Xiong 015; Shizhe Diao; Jianmeng Liu; Jipeng Zhang; Rui Pan 002; Haoxiang Wang 003; Wenbin Hu 002; Hanning Zhang; Hanze Dong; Renjie Pi; et al. (CoRR, 2023)

Gibbs Sampling from Human Feedback: A Provable KL- constrained Framework for RLHF. - Wei Xiong 015; Hanze Dong; Chenlu Ye; Han Zhong 001; Nan Jiang 008; Tong Zhang 001 (CoRR, 2023)

RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment. - Hanze Dong; Wei Xiong 015; Deepanshu Goyal; Yihan Zhang; Winnie Chow; Rui Pan 002; Shizhe Diao; Jipeng Zhang; Kashun Shum; Tong Zhang 001 (Trans. Mach. Learn. Res., 2023)

Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo. - Xunpeng Huang; Difan Zou; Hanze Dong; Yi-An Ma; Tong Zhang 001 (COLT, 2024)

Mitigating the Alignment Tax of RLHF. - Yong Lin; Hangyu Lin; Wei Xiong 015; Shizhe Diao; Jianmeng Liu; Jipeng Zhang; Rui Pan 002; Haoxiang Wang 003; Wenbin Hu 002; Hanning Zhang; Hanze Dong; Renjie Pi; et al. (EMNLP, 2024)

MLLM-Protector: Ensuring MLLM's Safety without Hurting Performance. - Renjie Pi; Tianyang Han; Jianshu Zhang 003; Yueqi Xie; Rui Pan 002; Qing Lian; Hanze Dong; Jipeng Zhang; Tong Zhang 001 (EMNLP, 2024)

FIRST: Teach A Reliable Large Language Model Through Efficient Trustworthy Distillation. - KaShun Shum; Minrui Xu; Jianshu Zhang 003; Zixin Chen; Shizhe Diao; Hanze Dong; Jipeng Zhang; Muhammad Omer Raza (EMNLP, 2024)

Reverse Diffusion Monte Carlo. - Xunpeng Huang; Hanze Dong; Yifan Hao 002; Yian Ma; Tong Zhang 001 (ICLR, 2024)

Spurious Feature Diversification Improves Out-of-distribution Generalization. - Yong Lin; Lu Tan; Yifan Hao 002; Honam Wong; Hanze Dong; Weizhong Zhang; Yujiu Yang; Tong Zhang 001 (ICLR, 2024)

Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-constraint. - Wei Xiong 015; Hanze Dong; Chenlu Ye; Ziqi Wang 003; Han Zhong 001; Heng Ji 001; Nan Jiang 008; Tong Zhang 001 (ICML, 2024)

Faster Sampling via Stochastic Gradient Proximal Sampler. - Xunpeng Huang; Difan Zou; Hanze Dong; Yian Ma; Tong Zhang 001 (ICML, 2024)

LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. - Shizhe Diao; Rui Pan 002; Hanze Dong; Kashun Shum; Jipeng Zhang; Wei Xiong 015; Tong Zhang 001 (NAACL (Demonstrations), 2024)

Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference. - Xunpeng Huang; Difan Zou; Hanze Dong; Yi Zhang; Yian Ma; Tong Zhang 001 (NeurIPS, 2024)

Online Iterative Reinforcement Learning from Human Feedback with General Preference Model. - Chenlu Ye; Wei Xiong 015; Yuheng Zhang; Hanze Dong; Nan Jiang 008; Tong Zhang 001 (NeurIPS, 2024)

Enhancing Multi-modal Regular Expression Synthesis via Large Language Models and Semantic Manipulations of Sub-expressions. - Zipan Tang; Yixuan Yan; Rongchen Li; Hanze Dong; Haiming Chen 001; Hongyu Gao (SETTA, 2024)

MLLM-Protector: Ensuring MLLM's Safety without Hurting Performance. - Renjie Pi; Tianyang Han; Yueqi Xie; Rui Pan 002; Qing Lian; Hanze Dong; Jipeng Zhang; Tong Zhang 001 (CoRR, 2024)

An Improved Analysis of Langevin Algorithms with Prior Diffusion for Non-Log-Concave Sampling. - Xunpeng Huang; Hanze Dong; Difan Zou; Tong Zhang 001 (CoRR, 2024)

ThinK: Thinner Key Cache by Query-Driven Pruning. - Yuhui Xu; Zhanming Jie; Hanze Dong; Lei Wang; Xudong Lu; Aojun Zhou; Amrita Saha; Caiming Xiong; Doyen Sahoo (CoRR, 2024)

MathHay: An Automated Benchmark for Long-Context Mathematical Reasoning in LLMs. - Lei Wang 185; Shan Dong; Yuhui Xu; Hanze Dong; Yalu Wang; Amrita Saha; Ee-Peng Lim; Caiming Xiong; Doyen Sahoo (CoRR, 2024)

Offline Reinforcement Learning for LLM Multi-Step Reasoning. - Huaijie Wang; Shibo Hao; Hanze Dong; Shenao Zhang; Yilin Bao; Ziran Yang; Yi Wu (CoRR, 2024)

RLHF Workflow: From Reward Modeling to Online RLHF. - Hanze Dong; Wei Xiong 015; Bo Pang 004; Haoxiang Wang 003; Han Zhao 002; Yingbo Zhou; Nan Jiang 008; Doyen Sahoo; Caiming Xiong; Tong Zhang 001 (Trans. Mach. Learn. Res., 2024)

Offline Reinforcement Learning for LLM Multi-step Reasoning. - Huaijie Wang; Shibo Hao; Hanze Dong; Shenao Zhang; Yilin Bao; Ziran Yang; Yi Wu 013 (ACL (Findings), 2025)

ThinK: Thinner Key Cache by Query-Driven Pruning. - Yuhui Xu; Zhanming Jie; Hanze Dong; Lei Wang 185; Xudong Lu; Aojun Zhou; Amrita Saha; Caiming Xiong; Doyen Sahoo (ICLR, 2025)

Automatic Curriculum Expert Iteration for Reliable LLM Reasoning. - Zirui Zhao; Hanze Dong; Amrita Saha; Caiming Xiong; Doyen Sahoo (ICLR, 2025)

Entropy-Regularized Process Reward Model. - Hanning Zhang; Pengcheng Wang 005; Shizhe Diao; Yong Lin; Rui Pan 002; Hanze Dong; Dylan Zhang; Pavlo Molchanov 001; Tong Zhang 001 (Trans. Mach. Learn. Res., 2025)

Reward-Guided Speculative Decoding for Efficient LLM Reasoning. - Baohao Liao; Yuhui Xu; Hanze Dong; Junnan Li 001; Christof Monz; Silvio Savarese; Doyen Sahoo; Caiming Xiong (CoRR, 2025)

BOLT: Bootstrap Long Chain-of-Thought in Language Models without Distillation. - Bo Pang 004; Hanze Dong; Jiacheng Xu 001; Silvio Savarese; Yingbo Zhou; Caiming Xiong (CoRR, 2025)

Reward Models Identify Consistency, Not Causality. - Yuhui Xu; Hanze Dong; Lei Wang; Caiming Xiong; Junnan Li (CoRR, 2025)

A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce. - Wei Xiong 015; Jiarui Yao; Yuhui Xu; Bo Pang 004; Lei Wang 185; Doyen Sahoo; Junnan Li 001; Nan Jiang 008; Tong Zhang 001; Caiming Xiong; Hanze Dong (CoRR, 2025)

Optimizing Chain-of-Thought Reasoners via Gradient Variance Minimization in Rejection Sampling and RL. - Jiarui Yao; Yifan Hao 002; Hanning Zhang; Hanze Dong; Wei Xiong 015; Nan Jiang 008; Tong Zhang 001 (CoRR, 2025)

Scalable Chain of Thoughts via Elastic Reasoning. - Yuhui Xu; Hanze Dong; Lei Wang 185; Doyen Sahoo; Junnan Li 001; Caiming Xiong (CoRR, 2025)

Beyond 'Aha!': Toward Systematic Meta-Abilities Alignment in Large Reasoning Models. - Zhiyuan Hu; Yibo Wang; Hanze Dong; Yuhui Xu; Amrita Saha; Caiming Xiong; Bryan Hooi; Junnan Li 001 (CoRR, 2025)

Fractured Chain-of-Thought Reasoning. - Baohao Liao; Hanze Dong; Yuhui Xu; Doyen Sahoo; Christof Monz; Junnan Li 001; Caiming Xiong (CoRR, 2025)

Almost Linear Convergence under Minimal Score Assumptions: Quantized Transition Diffusion. - Xunpeng Huang; Yingyu Lin; Nikki Lijing Kuang; Hanze Dong; Difan Zou; Yian Ma; Tong Zhang 001 (CoRR, 2025)