Publications

Conference Papers

2026

  • RH On measuring influence in avoiding undesired future.
    Lue Tao, Tian-Zuo Wang, Yuan Jiang, and Zhi-Hua Zhou.
    In: The 14th International Conference on Learning Representations (ICLR’26), Rio de Janeiro, Brazil, 2026.

2025

  • RH Variance-reduced long-term rehearsal learning with QP reformulation.
    Wen-Bo Du, Tian Qin, Tian-Zuo Wang, and Zhi-Hua Zhou.
    In: Advances in Neural Information Processing Systems 38 (NeurIPS’25), San Diego, CA, 2025.

  • CA Polynomial-delay MAG listing with novel locally complete orientation rules. (Oral, 0.99%, out of 12,107 papers)[PDF][Code]
    Tian-Zuo Wang, Wen-Bo Du, and Zhi-Hua Zhou.
    In: Proceedings of the 42nd International Conference on Machine Learning (ICML’25), Vancouver, Canada, 2025.

  • RH Enabling optimal decisions in rehearsal learning under CARE condition.[PDF]
    Wen-Bo Du, Hao-Yi Lei, Lue Tao, Tian-Zuo Wang, and Zhi-Hua Zhou.
    In: Proceedings of the 42nd International Conference on Machine Learning (ICML’25), Vancouver, Canada, 2025.

  • CA Strong and weak identifiability of optimization-based causal discovery.[PDF]
    Mingjia Li, Hong Qian, Tian-Zuo Wang, ShujunLi, Min Zhang, and Aimin Zhou.
    In: Proceedings of the 42nd International Conference on Machine Learning (ICML’25), Vancouver, Canada, 2025.

  • RH Avoiding undesired future with sequential decisions.[PDF]
    Lue Tao, Tian-Zuo Wang, Yuan Jiang, and Zhi-Hua Zhou.
    In: Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI’25), Montreal, Canada, 2025.

  • RH Gradient-based nonlinear rehearsal learning with multivariate alterations. (Oral, 1.54%, out of 12,957 papers)[PDF]
    Tian Qin, Tian-Zuo Wang, and Zhi-Hua Zhou.
    In: Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI’25), Philadelphia, PA, 2025.

2024

  • RH Avoiding undesired futures with minimal cost in non-stationary environments.[PDF]
    Wen-Bu Du, Tian Qin, Tian-Zuo Wang, and Zhi-Hua Zhou.
    In: Advances in Neural Information Processing Systems 37 (NeurIPS’24), Vancouver, Canada, 2024.

  • CA An efficient maximal ancestral graph listing algorithm. (Spotlight, 3.5%, out of 9,473 papers)[PDF][Code]
    Tian-Zuo Wang, Wen-Bo Du, and Zhi-Hua Zhou.
    In: Proceedings of the 41st International Conference on Machine Learning (ICML’24), Vienna, Austria, 2024.

2023

  • RH Rehearsal learning for avoiding undesired future.[PDF]
    Tian Qin, Tian-Zuo Wang, and Zhi-Hua Zhou.
    In: Advances in Neural Information Processing Systems 36 (NeurIPS’23), New Orleans, Louisiana, 2023.

  • CA Estimating possible causal effects with latent variables via adjustment.[PDF][Code]
    Tian-Zuo Wang, Tian Qin, and Zhi-Hua Zhou.
    In: Proceedings of the 40th International Conference on Machine Learning (ICML’23), Hawaii, Honolulu, 2023.

  • CA Learning causal structure on mixed data with tree-structured functional models.[PDF]
    Tian Qin, Tian-Zuo Wang, and Zhi-Hua Zhou.
    In: Proceedings of the 23rd SIAM International Conference on Data Mining (SDM’23), Minneapolis, Minnesota, 2023.

Before 2022

  • CA Sound and complete causal identification with latent variables given local background knowledge.[PDF][Code]
    Tian-Zuo Wang, Tian Qin, and Zhi-Hua Zhou.
    In: Advances in Neural Information Processing Systems 35 (NeurIPS’22), New Orleans, Louisiana, 2022.

  • CA Actively identifying causal effects with latent variables given only response variable observable.[PDF]
    Tian-Zuo Wang and Zhi-Hua Zhou.
    In: Advances in Neural Information Processing Systems 34 (NeurIPS’21), Online, 2021.

  • CA Budgeted heterogeneous treatment effect estimation.
    Tian Qin, Tian-Zuo Wang, and Zhi-Hua Zhou.
    In: Proceedings of the 38th International Conference on Machine Learning (ICML’21), Online, 2021.

  • CA Cost-effectively identifying causal effects when only response variable is observable.[PDF][Supp][Code][Poster]
    Tian-Zuo Wang, Xi-Zhu Wu, Sheng-Jun Huang, and Zhi-Hua Zhou.
    In: Proceedings of the 37th International Conference on Machine Learning (ICML’20), Online, 2020.

  • CA Towards identifying causal relation between instances and labels.[PDF][Code]
    Tian-Zuo Wang, Sheng-Jun Huang, and Zhi-Hua Zhou.
    In: Proceedings of the 19th SIAM International Conference on Data Mining (SDM’19), Alberta, Canada, 2019.

Journal Papers

  • CA Estimating possible causal effects with latent variables via adjustment and novel rule orientation.[PDF]
    Tian-Zuo Wang, Lue Tao, Tian Qin, and Zhi-Hua Zhou.
    Artificial Intelligence, 2025.

  • CA Gradient-based causal discovery with latent variables.[PDF]
    Haotian Ni, Tian-Zuo Wang, Hong Tao, Xiuqi Huang, and Chenping Hou.
    Machine Learning, 2024.

  • CA Tracking treatment effect heterogeneity in evolving environments.[PDF]
    Tian Qin, Long-Fei Li, Tian-Zuo Wang, and Zhi-Hua Zhou.
    Machine Learning, 2023

  • CA 基于专家知识的主动因果效应辨识.[PDF]
    王天佐, 周志华.
    中国科学：信息科学, 2023.

  • CA Sound and complete causal identification with latent variables given local background knowledge.[PDF]
    Tian-Zuo Wang, Tian Qin, and Zhi-Hua Zhou.
    Artificial Intelligence, 2023

CA denotes causality and RH denotes rehearsal learning.