[1] X. Du, S. Ramamoorthy, W. Duivesteijn, J. Tian, M. Pechenizkiy,

Beyond Discriminant Patterns: On the Robustness of Decision Rule Ensembles. The IEEE International Conference on Data Mining (ICDM), 2025, https://arxiv.org/abs/2109.10432

[2] X. Du, S. Yang, W. Duivesteijn, M. Pechenizkiy,

Conformalized Exceptional Model Mining: Telling Where Your Model Performs (Not) Well. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2025, https://arxiv.org/abs/2508.15569

[3] X. Du, Y. Pei, W. Duivesteijn, M. Pechenizkiy,

Exceptional Spatio-Temporal Behavior Mining through Bayesian Non-Parametric Modeling.

Data Mining and Knowledge Discovery (ECML-PKDD Journal Track), 2020, 34, 1267-1290, https://link.springer.com/article/10.1007/s10618-020-00674-z

[4] X. Du, Y. Pei, W. Duivesteijn, M. Pechenizkiy,

Fairness in Network Representation by Latent Structural Heterogeneity in Observational

Data. AAAI Conference on Artificial Intelligence (AAAI), 2020, (Vol. 34, No. 04, pp. 3809-3816), https://ojs.aaai.org/index.php/AAAI/article/view/5792

[5] X. Du, L. Sun, W. Duivesteijn, A. Nikolaev and M. Pechenizkiy,

Adversarial Representation Learning for Causal Effect Inference with Observational

Data. Data Mining and Knowledge Discovery, 2021, 35(4), 1713-1738, https://link.springer.com/article/10.1007/s10618-021-00759-3

[6] X. Du, W. Duivesteijn, M. Klabbers, M. Pechenizkiy,

ELBA: Exceptional Learning Behavior Analysis.

Proceedings of the Eleventh International Conference on Educational Data Mining (EDM),

2018, https://eric.ed.gov/?id=ED593224

[7] X. Du, B. Legastelois, B. Ganesh, A. Rajan, H. Chockler, V. Belle, S. Anderson, S. Ramamoorthy,

Vision Checklist: Testable Error Analysis of Image Models to Help System

Designers Interrogate Model Capabilities. 2022, https://arxiv.org/abs/2201.11674

[8] X. Du, A. Nikolaev, W. Duivesteijn, M. Pechenizkiy,

Propensity Guided Transformer for Causal Effect Inference. 2024, Under Review

[9] Y. Pei, X. Du, J. Zhang, G. Fletcher, M. Pechenizkiy,

struc2gauss: Structure Preserving Network Embedding via Gaussian Embedding.

Data Mining and Knowledge Discovery, 2020, https://link.springer.com/article/10.1007/s10618-020-00684-x

[10] Y. Wang, V. Menkovski, H. Wang, X. Du, M. Pechenizkiy,

Causal Discovery from Incomplete Data: A Deep Learning Approach. arxiv preprint, 2020, https://arxiv.org/abs/2001.05343

[11] Anthony L. Corso, Sydney M. Katz, Craig Innes, Xin Du, Subramanian Ramamoorthy, Mykel J. Kochenderfer,

Risk-Driven Design of Perception Systems. The 36th Conference on Neural Information Processing Systems, 2022,

https://www.research.ed.ac.uk/en/publications/risk-driven-design-of-perception-systems

软件

ABCEI， 基于对抗学习的因果推断软件，https://github.com/octeufer/Adversarial-Balancing-based-representation-learning-for-Causal-Effect-Inference

Annotate_Optimize, 基于组合优化方法的点状注记优化软件，https://github.com/octeufer/Annotate_Optimize