个人简介

曾艳，讲师，硕士生导师，广东工业大学博士，日本理化学研究所联合培养博士，清华大学计算机科学与技术系博士后。

研究兴趣

主要研究因果推断、因果强化学习、机器学习等理论与应用研究。

主讲课程

本科生课程《概率论与数理统计》、《分布式并行计算》、《数据科学的概率基础》。

学习经历

2012年9月-2016年7月，广东工业大学，应用数学学院，理学学士；

2016年9月-2021年6月，广东工业大学，计算机学院，工学博士；

2019年10月-2020年10月，日本理化学研究所，因果推断团队，联合培养博士。

工作经历

2021年7月-2023年8月，清华大学，计算机科学与技术系，博士后；

2023年9月至今，William威廉，William威廉，讲师。

主要获奖荣誉

2024年，William威廉工会积极分子。

主要科研项目

主持市教委科研计划科技一般项目1项、中国博士后科学基金面上项目1项，参与国家自然科学基金项目1项。主要有：

1. 市教委科研计划科技一般项目，面向灵巧操作任务的因果强化学习方法研究，2024年01月至2026年12月，10万元，主持人。

2. 中国博士后科学基金面上项目，面向复杂任务的因果强化学习方法研究，2022年06月至2023年07月，8万元，主持人（已结题）。

3. 国家自然科学基金项目，高阶网络模体聚类算法与应用研究，2020年01月至2023年12月，61万元，参与人（已结题）。

主要学术成果

发表论文20余篇，主要有：

[1]. Ruichu Cai, Siyang Huang, Jie Qiao, Wei Chen, Yan Zeng, Keli Zhang, Fuchun Sun, Yang Yu and Zhifeng Hao. Learning by doing: an online causal reinforcement learning framework with causal-aware policy[J]. Science China Information Sciences, 2026, 69(2): 122104.

[2]. Peng Wu, Haoxuan Li, Chunyuan Zheng, Yan Zeng, Jiawei Chen, Yang Liu, Ruocheng Guo and Kun Zhang. Learning Counterfactual Outcomes Under Rank Preservation[C]. NeurIPS 2025.

[3]. Zheng Li, Xichen Guo, Feng Xie, Yan Zeng, Hao Zhang and Zhi Geng. Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables[C]. NeurIPS 2025.

[4]. Xichen Guo, Feng Xie, Yan Zeng, Hao Zhang and Zhi Geng. Data-Driven Selection of Instrumental Variables for Additive Nonlinear, Constant Effects Models[C]. International Conference on Machine Learning, 2025.

[5]. Yan Zeng, Ruichu Cai, Fuchun Sun*, Libo Huang, Zhifeng Hao. A Survey on Causal Reinforcement Learning [J]. IEEE Transactions on Neural Networks and Learning Systems, 2024.

[6]. Feng Xie, Zheng Li, Peng Wu, Yan Zeng*, Chunchen Liu, and Zhi Geng. Local Causal Structure Learning in the Presence of Latent Variables [C]. International Conference on Machine Learning (ICML), Vienna, Austria, 2024.

[7]. Peng Wu, Ziyu Shen, Feng Xie, Zhongyao Wang, Chunchen Liu, and Yan Zeng*. Policy Learning for Balancing Short-Term and Long-Term Rewards [C]. International Conference on Machine Learning (ICML), Vienna, Austria, 2024.

[8]. Tianying Ji, Yongyuan Liang, Yan Zeng, Yu Luo, Guowei Xu, Jiawei Guo, Ruijie Zheng, Furong Huang, Fuchun Sun, Huazhe Xu. ACE: Off-Policy Actor-Critic with Causality-Aware Entropy Regularization [C]. International Conference on Machine Learning (ICML), Vienna, Austria, 2024.

[9]. Feng Xie, Zhen Yao, Lin Xie, Yan Zeng*, and Zhi Geng. Identification and Estimation of the Bi-Directional MR with Some Invalid Instruments. NeurIPS 2024 (oral).

[10]. Libo Huang, Yan Zeng, Chuanguang Yang, Zhulin An, Yongjun Xu. e-Tag: Class-Incremental Learning with Hierarchical Embedding Distillation and Task-Oriented Generation. The 38th Annual AAAI Conference on Artificial Intelligence, 2024.

[11]. Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takshi Nicholas Maeda, Shohei Shimizu*. Python package for causal discovery based on LiNGAM[J]. Journal of Machine Learning Research, 2023, 24(14): 1-8.

[12]. Feng Xie, Yan Zeng, Yangbo He*, Zhengming Chen, Zhi Geng. Causal Discovery of 1-Factor Measurement Models in Linear Latent Variable Models with Arbitrary Noise Distributions[J]. Neurocomputing, 2023.

[13]. Libo Huang, Gan Lu, Yan Zeng, and Wing-Kuen Ling. Automatical Spike Sorting with Low-Rank and Sparse Representation[J]. IEEE Transactions on Biomedical Engineering, 2023.

[14]. Yan Zeng, Shohei Shimizu, Hidetoshi Matsui, Fuchun Sun. Causal Discovery for Linear Mixed Data [C]. Conference on Causal Learning and Reasoning. PMLR, 2022: 994-1009.

[15]. Siyang Huang#, Yan Zeng#, Ruichu Cai, Zhifeng Hao, and Fuchun Sun. Offline Causal Imitation Learning with Latent Confounders [C]. International Conference on Cognitive Computation and Systems. 2022.

[16]. Yan Zeng, Zhifeng Hao*, Ruichu Cai*, Feng Xie, Libo Huang, Shohei Shimizu. Nonlinear Causal Discovery with Multiple High-Dimensional Observations [J]. IEEE Transactions on Neural Networks and Learning Systems, 2021.

[17]. Yan Zeng, Shohei Shimizu*, Ruichu Cai, Feng Xie, Michio Yamamoto, Zhifeng Hao. Causal discovery with multi-domain LiNGAM for latent factors[C]. International Joint Conferences on Artificial Intelligence (IJCAI), 2021.

[18]. Yan Zeng, Zhifeng Hao*, Ruichu Cai*, et al. A causal discovery algorithm based on the prior selection of leaf nodes [J]. Neural Networks, 2020, 124: 130-145.

[19]. Libo Huang*, Wing-Kuen Ling, Yan Zeng. Spike Sorting Based On Low-Rank And Sparse Representation[C]//2020 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2020: 1-6.

[20]. Feng Xie, Ruichu Cai*, Yan Zeng, et al. An Efficient Entropy-Based Causal Discovery Method for Linear Structural Equation Models with IID Noise Variables [J]. IEEE Transactions on Neural Networks and Learning Systems, 2019, 31(5): 1667-1680.

[21]. Feng Xie, Ruichu Cai*, Yan Zeng, et al. Causal Discovery of Linear Non-Gaussian Acyclic Model with Small Samples[C]//International Conference on Intelligent Science and Big Data Engineering. Springer, Cham, 2019: 381-393.

[22]. Libo Huang, Wing-Kuen Ling*, Ruichu Cai, Yan Zeng. WMsorting: wavelet packets’ decomposition and mutual information-based spike sorting method[J]. IEEE Transactions on Nanobioscience, 2019, 18(3): 283-295.