Publications

2026

1. 

   Are Two Datasets Close Enough With Statistical Significance?
   A Kernel Distributional Closeness Testing Approach

   Z. Zhou, L. Peng, X. Tian, M. Gong, and F. Liu

   In ICML, 2026

   Bib PDF Code

   @inproceedings{Zhou:Peng:Tian:Gong:Liu2026,
     author = {Zhou, Z. and Peng, L. and Tian, X. and Gong, M. and Liu, F.},
     title = {Are Two Datasets Close Enough With Statistical Significance?<br>A Kernel Distributional Closeness Testing Approach},
     booktitle = {ICML},
     year = {2026}
   }

2. 

   LOTTERY: Learning from Reference-Only Samples in Two-Sample Testing under Size Asymmetry

   X. Tian^*, Z. Zhou^*, L. Peng, and F. Liu

   In ICML, 2026

   ^* Equal contribution

   Bib PDF Code

   @inproceedings{Tian:Zhou:Peng:Liu2026,
     author = {Tian, X. and Zhou, Z. and Peng, L. and Liu, F.},
     title = {{LOTTERY:} Learning from Reference-Only Samples in Two-Sample Testing
                       under Size Asymmetry},
     booktitle = {ICML},
     note = {<sup>*</sup> Equal contribution},
     year = {2026}
   }

3. 

   CELEUS: Certifiable and Efficient LLM Evaluation via E-Processes

   Z. Zhou^*, Z. Ye^*, Z. Chen, B. Li, and F. Liu

   In ICML2026 Workshop on Hypothesis Testing, 2026

   ^* Equal contribution

   Bib PDF Code

   @inproceedings{Zhou:Ye:Chen:Li:Liu2026,
     author = {Zhou, Z. and Ye, Z. and Chen, Z. and Li, B. and Liu, F.},
     title = {{CELEUS:} Certifiable and Efficient {LLM} Evaluation via E-Processes},
     booktitle = {ICML2026 Workshop on Hypothesis Testing},
     note = {<sup>*</sup> Equal contribution},
     year = {2026}
   }

4. 

   USAD: Uncertainty-aware Statistical Adversarial Detection

   Z. Zhou^*, X. Tian^*, J. Zhang^*, Z. Ye, Y. Guo, D. Zhang, L. Peng, and F. Liu

   In ICML2026 Workshop on Hypothesis Testing, 2026

   ^* Equal contribution

   Bib PDF Code

   @inproceedings{Zhou:Tian:Zhang:Ye:Guo:Zhang:Peng:Liu2026,
     author = {Zhou, Z. and Tian, X. and Zhang, J. and Ye, Z. and Guo, Y. and Zhang, D. and Peng, L. and Liu, F.},
     title = {{USAD:} Uncertainty-aware Statistical Adversarial Detection},
     booktitle = {ICML2026 Workshop on Hypothesis Testing},
     note = {<sup>*</sup> Equal contribution},
     year = {2026}
   }

2025

1. 

   DUAL: Learning Diverse Kernels for Aggregated Two-sample and Independence Testing

   Z. Zhou^*, X. Tian^*, L. Peng, C. Lei, A. Schrab, D. J. Sutherland, and F. Liu

   In NeurIPS, 2025

   ^* Equal contribution

   Bib PDF Code

   @inproceedings{Zhou:Tian:Peng:Lei:Schrab:Sutherland:Liu2025,
     author = {Zhou, Z. and Tian, X. and Peng, L. and Lei, C. and Schrab, A. and Sutherland, D.~J. and Liu, F.},
     title = {{DUAL:} Learning Diverse Kernels for Aggregated Two-sample and Independence
                       Testing},
     booktitle = {NeurIPS},
     note = {<sup>*</sup> Equal contribution},
     year = {2025}
   }

2. 

   Anchor-based Maximum Discrepancy for Relative Similarity Testing

   Z. Zhou, L. Peng, X. Tian, and F. Liu

   In NeurIPS, 2025

   Bib PDF Code

   @inproceedings{Zhou:Peng:Tian:Liu2025,
     author = {Zhou, Z. and Peng, L. and Tian, X. and Liu, F.},
     title = {Anchor-based Maximum Discrepancy for Relative Similarity Testing},
     booktitle = {NeurIPS},
     year = {2025}
   }

3. 

   A Unified Data Representation Learning for Non-parametric Two-sample Testing

   X. Tian, L. Peng, Z. Zhou, M. Gong, A. Gretton, and F. Liu

   In UAI, 2025

   Bib PDF

   @inproceedings{Tian:Peng:Zhou:Gong:Gretton:Liu2025,
     author = {Tian, X. and Peng, L. and Zhou, Z. and Gong, M. and Gretton, A. and Liu, F.},
     title = {A Unified Data Representation Learning for Non-parametric Two-sample
                       Testing},
     booktitle = {UAI},
     year = {2025}
   }

4. 

   Interpretation with baseline shapley value for feature groups on tree models

   F. Xu, Z. Zhou, J. Ni, and W. Gao

   Frontiers of Computer Science, 2025

   Bib PDF

   @article{xu2025interpretation,
     title = {Interpretation with baseline shapley value for feature groups on tree models},
     author = {Xu, F. and Zhou, Z. and Ni, J. and Gao, W.},
     journal = {Frontiers of Computer Science},
     volume = {19},
     number = {5},
     pages = {195316},
     year = {2025}
   }

2023

1. 

   On the Exploration of Local Significant Differences for Two-Sample Test

   Z. Zhou, J. Ni, J. Yao, and W. Gao

   In NeurIPS, 2023

   Bib PDF Code

   @inproceedings{Zhou:Ni:Yao:Gao2023,
     author = {Zhou, Z. and Ni, J. and Yao, J. and Gao, W.},
     title = {On the Exploration of Local Significant Differences for Two-Sample Test},
     booktitle = {NeurIPS},
     year = {2023}
   }

2021

1. 

   Active learning for multi-objective optimal road congestion pricing considering negative land use effect

   S. Zhong, Y. Gong, Z. Zhou, R. Cheng, and F. Xiao

   Transportation Research Part C: Emerging Technologies, 2021

   Bib PDF

   @article{Zhong:Gong:Zhou:Cheng:Xiao,
     title = {Active learning for multi-objective optimal road congestion pricing considering negative land use effect},
     author = {Zhong, S. and Gong, Y. and Zhou, Z. and Cheng, R. and Xiao, F.},
     journal = {Transportation Research Part C: Emerging Technologies},
     volume = {125},
     pages = {103002},
     year = {2021}
   }