AI For Biology:
GenomeOcean: An Efficient Genome Foundation Model Trained on Large-Scale Metagenomic Assemblies
Zhihan Zhou, Robert Riley, Satria Kautsar, Weimin Wu, ..., Han Liu, Zhong Wang
DNABERT-S: LEARNING SPECIES-AWARE DNA EMBEDDING WITH GENOME FOUNDATION MODELS
Bioinformatics & Intelligent Systems for Molecular Biology (ISMB) 2025
Zhihan Zhou*, Weimin Wu*, Harrison Ho, Jiayi Wang, Lizhen Shi, Ramana V Davuluri, Zhong Wang, Han Liu
Genome-Factory: An Integrated Library for Tuning, Deploying, and Interpreting Genomic Models
Weimin Wu*, Xuefeng Song*, Yibo Wen*, Qinjie Lin, Zhihan Zhou, Jerry Yao-Chieh Hu, Zhong Wang, Han Liu
POLO: Preference-Guided Multi-Turn Reinforcement Learning for Lead Optimization
Ziqing Wang, Yibo Wen, William Pattie, Xiao Luo, Weimin Wu, Jerry Yao-Chieh Hu, Abhishek Pandey, Han Liu, Kaize Ding
Zhihan Zhou, Robert Riley, Satria Kautsar, Weimin Wu, ..., Han Liu, Zhong Wang
DNABERT-S: LEARNING SPECIES-AWARE DNA EMBEDDING WITH GENOME FOUNDATION MODELS
Bioinformatics & Intelligent Systems for Molecular Biology (ISMB) 2025
Zhihan Zhou*, Weimin Wu*, Harrison Ho, Jiayi Wang, Lizhen Shi, Ramana V Davuluri, Zhong Wang, Han Liu
Genome-Factory: An Integrated Library for Tuning, Deploying, and Interpreting Genomic Models
Weimin Wu*, Xuefeng Song*, Yibo Wen*, Qinjie Lin, Zhihan Zhou, Jerry Yao-Chieh Hu, Zhong Wang, Han Liu
POLO: Preference-Guided Multi-Turn Reinforcement Learning for Lead Optimization
Ziqing Wang, Yibo Wen, William Pattie, Xiao Luo, Weimin Wu, Jerry Yao-Chieh Hu, Abhishek Pandey, Han Liu, Kaize Ding
LLM Theory:
In-Context Deep Learning via Transformer Models
International Conference on Machine Learning (ICML) 2025
Weimin Wu*, Maojiang Su*, Jerry Yao-Chieh Hu*, Zhao Song, Han Liu
In-Context Learning as Conditioned Associative Memory Retrieval
International Conference on Machine Learning (ICML) 2025
Weimin Wu*, Teng-Yun Hsiao*, Jerry Yao-Chieh Hu*, Wenxin Zhang, Han Liu
On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality
International Conference on Learning Representations (ICLR) 2025
Jerry Yao-Chieh Hu*, Weimin Wu*, Yi-Chen Lee*, Yu-Chao Huang*, Minshuo Chen, Han Liu
On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs)
Advances in Neural Information Processing Systems 38 (NeurIPS) 2024
Jerry Yao-Chieh Hu*, Weimin Wu*, Zhuoru Li, Zhao Song, Han Liu
Universal Approximation with Softmax Attention
Jerry Yao-Chieh Hu, Hude Liu, Hong-Yu Chen, Weimin Wu, Han Liu
International Conference on Machine Learning (ICML) 2025
Weimin Wu*, Maojiang Su*, Jerry Yao-Chieh Hu*, Zhao Song, Han Liu
In-Context Learning as Conditioned Associative Memory Retrieval
International Conference on Machine Learning (ICML) 2025
Weimin Wu*, Teng-Yun Hsiao*, Jerry Yao-Chieh Hu*, Wenxin Zhang, Han Liu
On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality
International Conference on Learning Representations (ICLR) 2025
Jerry Yao-Chieh Hu*, Weimin Wu*, Yi-Chen Lee*, Yu-Chao Huang*, Minshuo Chen, Han Liu
On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs)
Advances in Neural Information Processing Systems 38 (NeurIPS) 2024
Jerry Yao-Chieh Hu*, Weimin Wu*, Zhuoru Li, Zhao Song, Han Liu
Universal Approximation with Softmax Attention
Jerry Yao-Chieh Hu, Hude Liu, Hong-Yu Chen, Weimin Wu, Han Liu
Additional Research:
Sci2Pol: Evaluating and Fine-tuning LLMs on Scientific-to-Policy Brief Generation
Weimin Wu, Alexander C. Furnas, Eddie Yang, Gefei Liu, Akhil Pandey Akella, Xuefeng Song, Dashun Wang, Han Liu
Instance-aware Model Ensemble With Distillation For Unsupervised Domain Adaptation
Weimin Wu, Jiayuan Fan, Tao Chen, Hancheng Ye, Bo Zhang, Baopu Li
Weimin Wu, Alexander C. Furnas, Eddie Yang, Gefei Liu, Akhil Pandey Akella, Xuefeng Song, Dashun Wang, Han Liu
Instance-aware Model Ensemble With Distillation For Unsupervised Domain Adaptation
Weimin Wu, Jiayuan Fan, Tao Chen, Hancheng Ye, Bo Zhang, Baopu Li