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曾艳

曾艳

讲师
北京工商大学，数学与统计学院
阜成路校区：北京市海淀区阜成路11号、33号（100048）

良乡主校区：北京市良乡高教园区北京工商大学数统楼（102488）
scholar.google.com/Yan Zeng
github.com/Yan Zeng
linkedin.com/in/yan-zeng
orcid.org/0000-0001-7721-2560
学生招生硕士研究生招生

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个人简介 领英

我是北京工商大学应用统计系讲师，隶属于耿直教授带领的因果推断团队。同时，我也很荣幸担任日本理化学研究所的访问学者，与 清水昌平教授开展合作。 在加入北京工商大学之前，我于清华大学计算机系从事博士后研究工作（2021–2023），合作导师为孙富春教授，主要研究方向为因果强化学习。 我于广东工业大学获得工学博士学位（2016–2021），导师为郝志峰教授和蔡瑞初教授，并由清水昌平教授联合指导。期间（2019–2020），我曾作为访问博士生在日本理化学研究所因果推断团队进行交流学习。

研究兴趣 团队主页

我的研究聚焦于因果机器学习，致力于将因果理论与可靠的决策与学习系统相结合。具体研究方向分为以下两个核心板块：

最新动态

发表论文 谷歌学术

# Corresponding author, * Equal contribution.

2026

Local Covariate Selection for Average Causal Effect Estimation without Pretreatment and Causal Sufficiency Assumptions
Zeyu Liu, Zheng Li, Feng Xie#, Yan Zeng#, Hao Zhang, Kun Zhang
International Conference on Machine Learning (ICML), Seoul, South Korea, 2026.
Spotlight, TOP 2.6% PDF Poster 
    @inproceedings{liu2026local,
        title={Local Covariate Selection for Average Causal Effect Estimation without Pretreatment and Causal Sufficiency Assumptions},
        author={Liu, Zeyu and Li, Zheng and Xie, Feng and Zeng, Yan and Zhang, Hao and Zhang, Kun},
        booktitle={International Conference on Machine Learning},
        year={2026}
    }
Testability of Instrumental Variables in Additive Nonlinear, Non-Constant Effects Models
Xichen Guo, Zheng Li, Biwei Huang, Yan Zeng, Zhi Geng, Feng Xie#
Journal of Machine Learning Research (JMLR), 2026.
arXiv 
        @article{guo2026testability,
          title={Testability of Instrumental Variables in Additive Nonlinear, Non-Constant Effects Models},
          author={Guo, Xichen and Li, Zheng and Huang, Biwei and Zeng, Yan and Geng, Zhi and Xie, Feng},
          journal={Journal of Machine Learning Research},
          year={2026}
        }
PrePrompt: Predictive Prompting for Class Incremental Learning
Libo Huang, Xiangqi Li, Jiarui Zhao, Zhulin An#, Chuanguang Yang, Boyu Diao, Fei Wang, Yan Zeng, Zhifeng Hao, Yongjun Xu
SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2026. [CCF-A]
Project PDF arXiv Code 
        @inproceedings{huang2026preprompt,
        title={PrePrompt: Predictive prompting for class incremental learning},
        author={Libo Huang, Xiangqi Li, Jiarui Zhao, Zhulin An, Chuanguang Yang, Boyu Diao, Fei Wang,  Yan Zeng, Zhifeng Hao, Yongjun Xu},
        booktitle={Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)},
        year={2026}
        }
Learning by Doing: An Online Causal Reinforcement Learning Framework with Causal-Aware Policy
Ruichu Cai, Siyang Huang, Jie Qiao, Wei Chen, Yan Zeng, Kun Zhang, Fuchun Sun, Yang Yu, Zhifeng Hao
Science China Information Sciences, 2026, 69(2): 122104.
PDF 
        @article{cai2026learning,
          title={Learning by doing: an online causal reinforcement learning framework with causal-aware policy},
          author={Cai, Ruichu and Huang, Siyang and Qiao, Jie and Chen, Wei and Zeng, Yan and Zhang, Kun and Sun, Fuchun and Yu, Yang and Hao, Zhifeng},
          journal={Science China Information Sciences},
          volume={69},
          number={2},
          pages={122104},
          year={2026}
        }

2025

A Survey on Causal Reinforcement Learning
Yan Zeng, Ruichu Cai, Fuchun Sun, Libo Huang, Zhifeng Hao
IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2025.
Project PDF 
        @article{zeng2025survey,
            title={A Survey on Causal Reinforcement Learning},
            author={Zeng, Yan and Cai, Ruichu and Sun, Fuchun and Huang, Libo and Hao, Zhifeng},
            journal={IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS)},
            year={2025},
            volume={36},
            number={4},
            pages={5942-5962},
            publisher={IEEE}
        }
Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables
Zheng Li, Xichen Guo, Feng Xie#, Yan Zeng, Hao Zhang#, Zhi Geng
Neural Information Processing Systems (NeurIPS), San Diego, USA, 2025.
PDF 
        @inproceedings{li2025local,
            title={Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables},
            author={Li, Zheng and Guo, Xichen and Xie, Feng and Zeng, Yan and Zhang, Hao and Geng, Zhi},
            booktitle={Advances in Neural Information Processing Systems},
            year={2025}
        }
Learning Counterfactual Outcomes Under Rank Preservation
Peng Wu, Haoxuan Li, Chunyuan Zheng, Yan Zeng, Jiawei Chen, Yang Liu, Ruocheng Guo, Kun Zhang
Neural Information Processing Systems (NeurIPS), San Diego, USA, 2025.
PDF 
    @inproceedings{wu2025learning,
    title={Learning Counterfactual Outcomes Under Rank Preservation},
    author={Wu, Peng and Li, Haoxuan and Zheng, Chunyuan and Zeng, Yan and Chen, Jiawei and Liu, Yang and Guo, Ruocheng and Zhang, Kun},
    booktitle={Advances in Neural Information Processing Systems},
    year={2025}
    }
Data-Driven Selection of Instrumental Variables for Additive Nonlinear, Constant Effects Models
Xichen Guo, Feng Xie#, Yan Zeng, Hao Zhang, Zhi Geng
International Conference on Machine Learning (ICML), Vancouver, Canada, 2025.
PDF 
        @inproceedings{guo2025datadriven,
            title={Data-Driven Selection of Instrumental Variables for Additive Nonlinear, Constant Effects Models},
            author={Guo, Xichen and Xie, Feng and Zeng, Yan and Zhang, Hao and Geng, Zhi},
            booktitle={International Conference on Machine Learning},
            year={2025}
        }

2024

Identification and Estimation of the Bi-Directional MR with Some Invalid Instruments
Feng Xie, Zhen Yao, Lin Xie, Yan Zeng#, Zhi Geng
Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2024.
Oral, TOP 0.39% PDF 
        @inproceedings{xie2024identification,
          title={Identification and Estimation of the Bi-Directional MR with Some Invalid Instruments},
          author={Xie, Feng and Yao, Zhen and Xie, Lin and Zeng, Yan and Geng, Zhi},
          booktitle={Advances in Neural Information Processing Systems},
          year={2024}
        }
Local Causal Structure Learning in the Presence of Latent Variables
Feng Xie, Zheng Li, Peng Wu, Yan Zeng#, Chunchen Liu, Zhi Geng
International Conference on Machine Learning (ICML), 2024.
PDF Code 
    @inproceedings{xie2024local,
      title={Local Causal Structure Learning in the Presence of Latent Variables},
      author={Xie, Feng and Li, Zheng and Wu, Peng and Zeng, Yan and Liu, Chunchen and Geng, Zhi},
      booktitle={International Conference on Machine Learning},
      year={2024}
    }
Policy Learning for Balancing Short-Term and Long-Term Rewards
Peng Wu, Ziyu Shen, Feng Xie, Zhongyao Wang, Chunchen Liu, Yan Zeng#
International Conference on Machine Learning (ICML), 2024.
PDF Code 
    @inproceedings{wu2024policy,
      title={Policy Learning for Balancing Short-Term and Long-Term Rewards},
      author={Wu, Peng and Shen, Ziyu and Xie, Feng and Wang, Zhongyao and Liu, Chunchen and Zeng, Yan},
      booktitle={International Conference on Machine Learning},
      year={2024}
    }
ACE: Off-Policy Actor-Critic with Causality-Aware Entropy Regularization
Tianying Ji, Yongyuan Liang, Yan Zeng, Yu Luo, Guowei Xu, Jiawei Guo, Ruijie Zheng, Furong Huang, Fuchun Sun, Huazhe Xu
International Conference on Machine Learning (ICML), 2024.
Oral, TOP 1.5% Project PDF Code 
    @inproceedings{ji2024ace,
      title={ACE: Off-Policy Actor-Critic with Causality-Aware Entropy Regularization},
      author={Ji, Tianying and Liang, Yongyuan and Zeng, Yan and Luo, Yu and Xu, Guowei and Guo, Jiawei and Zheng, Ruijie and Huang, Furong and Sun, Fuchun and Xu, Huazhe},
      booktitle={International Conference on Machine Learning},
      year={2024}
    }
Learning the Optimal Policy for Balancing Multiple Short-Term and Long-Term Rewards
Qinwei Yang, Xueqing Liu, Yan Zeng, Ruocheng Guo, Yang Liu, Peng Wu#
Neural Information Processing Systems (NeurIPS), 2024.
PDF 
        @inproceedings{yang2024learning,
          title={Learning the Optimal Policy for Balancing Multiple Short-Term and Long-Term Rewards},
          author={Yang, Qinwei and Liu, Xueqing and Zeng, Yan and Guo, Ruocheng and Liu, Yang and Wu, Peng},
          booktitle={Advances in Neural Information Processing Systems},
          year={2024}
        }
Causal Discovery with Hidden Variables Based on Non-Gaussianity and Nonlinearity
Takashi Nicholas Maeda, Yan Zeng, Shohei Shimizu
Dependent Data in Social Sciences Research: Forms, Issues, and Methods of Analysis. Springer, 2024: 181-205.
PDF 
        @incollection{maeda2024causal,
            title={Causal Discovery with Hidden Variables Based on Non-Gaussianity and Nonlinearity},
            author={Maeda, Takashi Nicholas and Zeng, Yan and Shimizu, Shohei},
            booktitle={Dependent Data in Social Sciences Research: Forms, Issues, and Methods of Analysis},
            pages={181--205},
            year={2024},
            publisher={Springer}
        }
eTag: Class-Incremental Learning via Hierarchical Embedding Distillation and Task-Oriented Generation
Libo Huang, Yan Zeng, Chuanguang Yang, Zhulin An#, Boyu Diao, Yongjun Xu
AAAI Conference on Artificial Intelligence (AAAI), 2024.
PDF Slides Code 
    @inproceedings{huang2024etag,
        title={eTag: Class-Incremental Learning via Embedding Distillation and Task-Oriented Generation},
        author={Huang, Libo and Zeng, Yan and Yang, Chuanguang and An, Zhulin and Diao, Boyu and Xu, Yongjun},
        booktitle={Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
        volume={38},
        number={11},
        pages={12591--12599},
        year={2024}
    }
KFC: Knowledge Reconstruction and Feedback Consolidation Enable Efficient and Effective Continual Generative Learning
Libo Huang, Zhulin An#, Yan Zeng, Xiang Zhi, Yongjun Xu
International Conference on Learning Representations (ICLR) Tiny Papers Track, 2024
Oral PDF Code 
    @inproceedings{huang2024kfc,
        title={KFC: Knowledge Reconstruction and Feedback Consolidation Enable Efficient and Effective Continual Generative Learning},
        author={Huang, Libo and An, Zhulin and Zeng, Yan and Xu, Yongjun and others},
        booktitle={International Conference on Learning Representations (ICLR) Tiny Papers Track},
        year={2024}
    }

2023

Python package for causal discovery based on LiNGAM
Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, Shohei Shimizu
Journal of Machine Learning Research (JMLR), 2023, 24(14): 1-8.
PDF Code 
        @article{ikeuchi2023python,
          title={Python package for causal discovery based on LiNGAM},
          author={Ikeuchi, Takashi and Ide, Mayumi and Zeng, Yan and Maeda, Takashi Nicholas and Shimizu, Shohei},
          journal={Journal of Machine Learning Research},
          volume={24},
          number={14},
          pages={1--8},
          year={2023}
        }
Causal Discovery of 1-Factor Measurement Models in Linear Latent Variable Models with Arbitrary Noise Distributions
Feng Xie, Yan Zeng, Yangbo He, Zhengming Chen, Zhi Geng
Neurocomputing, 2023.
PDF 
        @article{xie2023causal,
            title={Causal Discovery of 1-Factor Measurement Models in Linear Latent Variable Models with Arbitrary Noise Distributions},
            author={Xie, Feng and Zeng, Yan and He, Yangbo and Chen, Zhengming and Geng, Zhi},
            journal={Neurocomputing},
            year={2023}
        }
Automatical Spike Sorting with Low-Rank and Sparse Representation
Libo Huang, Lu Gan, Yan Zeng, Bingo Wing-Kuen Ling#
IEEE Transactions on Biomedical Engineering (IEEE TBE), 2023.
PDF 
        @article{huang2023automatical,
            title={Automatical Spike Sorting with Low-Rank and Sparse Representation},
            author={Huang, Libo and Gan, Lu and Zeng, Yan and Ling, Bingo Wing-Kuen},
            journal={IEEE Transactions on Biomedical Engineering (IEEE TBE)},
            volume={71},
            number={5},
            pages={1677--1686},
            year={2023},
            publisher={IEEE}
        }

2022

Causal Discovery for Linear Mixed Data
Yan Zeng, Shohei Shimizu, Hidetoshi Matsui, Fuchun Sun
Conference on Causal Learning and Reasoning (CleaR), 2022.
PDF Code 
        @inproceedings{zeng2022causal,
          title={Causal Discovery for Linear Mixed Data},
          author={Zeng, Yan and Shimizu, Shohei and Matsui, Hidetoshi and Sun, Fuchun},
          booktitle={Conference on Causal Learning and Reasoning},
          pages={1018--1033},
          year={2022}
        }
Offline Causal Imitation Learning with Latent Confounders
Siyang Huang*, Yan Zeng*, Ruichu Cai, Zhifeng Hao, Fuchun Sun
International Conference on Cognitive Computation and Systems (CCIS), 2022.
PDF 
    @inproceedings{huang2022offline,
        title={Offline Causal Imitation Learning with Latent Confounders},
        author={Huang, Siyang and Zeng, Yan and Cai, Ruichu and Hao, Zhifeng and Sun, Fuchun},
        booktitle={International Conference on Cognitive Computation and Systems},
        year={2022}
    }

2021

Causal Discovery with Multi-Domain LiNGAM for Latent Factors
Yan Zeng, Shohei Shimizu, Ruichu Cai, Feng Xie, Michio Yamamoto, Zhifeng Hao
International Joint Conference on Artificial Intelligence (IJCAI), 2021.
PDF Code 
        @inproceedings{zeng2021causal,
            title={Causal discovery with multi-domain LiNGAM for latent factors},
            author={Zeng, Yan and Shimizu, Shohei and Cai, Ruichu and Xie, Feng and Yamamoto, Michio and Hao, Zhifeng},
            booktitle={International Joint Conference on Artificial Intelligence},
            year={2021}
        }
Nonlinear Causal Discovery with Multiple High-Dimensional Observations
Yan Zeng, Zhifeng Hao, Ruichu Cai, Feng Xie, Libo Huang, Shohei Shimizu
IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2021.
PDF Code 
    @article{zeng2021nonlinear,
        title={Nonlinear Causal Discovery with Multiple High-Dimensional Observations},
        author={Zeng, Yan and Hao, Zhifeng and Cai, Ruichu and Xie, Feng and Huang, Libo and Shimizu, Shohei},
        journal={IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS)},
        volume={34},
        number={5},
        pages={2234--2245},
        year={2021},
        publisher={IEEE}
    }
A Unified Optimization Model of Feature Extraction and Clustering for Spike Sorting
Libo Huang, Lu Gan, Bingo Wing-Kuen Ling#
IEEE Transactions on Neural Systems and Rehabilitation Engineering (IEEE TNSRE), 2021. [JCR-Q1]
PDF Code 
        @article{huang2021unified,
            title={A Unified Optimization Model of Feature Extraction and Clustering for Spike Sorting},
            author={Huang, Libo and Gan, Lu and Ling, Bingo Wing-Kuen},
            journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering (IEEE TNSRE)},
            volume={29},
            pages={750--759},
            year={2021},
            publisher={IEEE}
        }

Before 2020

A Causal Discovery Algorithm Based on the Prior Selection of Leaf Nodes
Yan Zeng, Zhifeng Hao, Ruichu Cai, Feng Xie, Liang Ou, Ruihui Huang
Neural Networks, 2020.
PDF 
        @article{zeng2020causal,
            title={A causal discovery algorithm based on the prior selection of leaf nodes},
            author={Zeng, Yan and Hao, Zhifeng and Cai, Ruichu and Xie, Feng and Ou, Liang and Huang, Ruihui},
            journal={Neural Networks},
            year={2020}
        }
An Efficient Entropy-Based Causal Discovery Method for Linear Structural Equation Models with IID Noise Variables
Feng Xie, Ruichu Cai, Yan Zeng, Jiantao Gao, Zhifeng Hao
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2019.
PDF 
    @article{xie2019efficient,
      title={An Efficient Entropy-Based Causal Discovery Method for Linear Structural Equation Models with IID Noise Variables},
      author={Xie, Feng and Cai, Ruichu and Zeng, Yan and Gao, Jiantao and Hao, Zhifeng},
      journal={IEEE Transactions on Neural Networks and Learning Systems},
      year={2019}
    }
Causal Discovery of Linear Non-Gaussian Acyclic Model with Small Samples
Feng Xie, Ruichu Cai, Yan Zeng, Zhifeng Hao
International Conference on Intelligence Science (IScIDE), Springer, 2019: 381-393.
PDF 
    @inproceedings{xie2019causal,
      title={Causal Discovery of Linear Non-Gaussian Acyclic Model with Small Samples},
      author={Xie, Feng and Cai, Ruichu and Zeng, Yan and Hao, Zhifeng},
      booktitle={International Conference on Intelligence Science},
      pages={381--393},
      year={2019}
    }
Spike Sorting Based On Low-Rank And Sparse Representation
Libo Huang#, Bingo Wing-Kuen Ling, Yan Zeng, Lu Gan
IEEE International Conference on Multimedia and Expo (ICME), 2020
Oral PDF Slides 
    @inproceedings{huang2020spike,
        title={Spike Sorting Based On Low-Rank And Sparse Representation},
        author={Huang, Libo and Ling, Bingo Wing-Kuen and Zeng, Yan and Gan, Lu},
        booktitle={IEEE International Conference on Multimedia and Expo (ICME)},
        pages={1--6},
        year={2020},
        organization={IEEE}
    }
WMsorting: Wavelet Packets' Decomposition and Mutual Information-based Spike Sorting Method
Libo Huang, Bingo Wing-Kuen Ling#, Ruichu Cai#, Yan Zeng, Jiong He, Yao Chen
IEEE Transactions on NanoBioscience (IEEE TNB), 2019
PDF 
    @article{huang2019wmsorting,
        title={WMsorting: Wavelet Packets' Decomposition and Mutual Information-based Spike Sorting Method},
        author={Huang, Libo and Ling, Bingo Wing-Kuen and Cai, Ruichu and Zeng, Yan and He, Jiong and Chen, Yao},
        journal={IEEE Transactions on NanoBioscience (IEEE TNB)},
        volume={18},
        number={3},
        pages={283--295},
        year={2019},
        publisher={IEEE}
    }

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