Lincen Yang
Postdoc Researcher in Interpretable Machine Learning & Data Mining (Email: ✉️)
Leiden University, The Netherlands
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Welcome to my personal website!
I am interested in fundamental methodology research that aims to develop human-understandable & trustworthy machine learning models, wich a focus on decision trees, probabilistic rules, histogram density estimations, and the Minimum Length Description (MDL) principle.
I collaborate with scientists and domain experts from various applications fields, including health care, chip making, and environmental science, to apply the developed methods & algorithms to use cases and try to answer real-world 'why' questions by discovering joint effects of feature variables.
During my PhD in Leiden I was supervised by Dr. Matthijs van Leeuwen.
News
News
(2025.11) 🎉 Very happy to share that we have one paper accepted at AAAI as Oral. It's my first work in causal inference and I will share more about it after we have the camera-ready version. Thank all co-authors, Zhong Li, Matthijs van Leeuwen, and Saber Salehkaleybar! See you all in Singapore 😀.
(2025.11) I will have a one-day research visit at the Algorithmics Group of the Faculty of Engineering, Mathematics and Computer Science of TU Delft on Nov 10th! Looking forward to continue our discussions since last time at SMILE!
(2025.10) I will give a talk at the next VUML seminar at VU Amsterdam on 27 of Oct, titled MDL-based Interpretable Machine Learning for High-stakes Applications.
(2025.10) Happy to share that our collaboration with Antwerp on applying cutting-edge interpretable machine learning algorithms to sustainable road maintenance has lead to a nice publication – our paper titled "Computational optimization for low-carbon and circular pavement management at the network level" has been accepted at the Resources, Conservation & Recycling Journal. Thank Dr. Zhaoxing Wang and looking forward to our further collaboration!
(2025.09) Our paper titled "Interpretable Machine Learning for Identifying ICU Readmission Risk in Subgroups with Probabilistic Rules" has been accepted to the medical informatics journal Journal of the American Medical Informatics Association (JAMIA). Thank our collaborators Siri van der Meijden (Healthplus.ai) & Sesmu Arbous (Leiden University Medical Center) and looking forward to our further collaborations on trustworthy AI in healthcare!
(2025.09) Our paper titled "Scalable, Explainable and Provably Robust Anomaly Detection with One-Step Flow Matching" has been accepted to NeurIPS 2025! Thank all our collaborators Zhong Li, Qi Huang, Yuxuan Zhu (and more)!
(2025.09) Will attend the farewell lecture of my academic grandfather Prof. Arno Siebes in Utrecht.
(2025.09) Our Human-Centered Data Mining (HuMine 2025) workshop at ECML-PKDD has finished. Thank all our participants for the excellent presentations and interesting discussions!
Publication List
Publication List
Conference
(AAAI) Yang, L, Li, Z, van Leeuwen, M & Salehkaleybar, S Learning Subgroups with Maximum Treatment Effects without Causal Heuristics. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2026), 2026.
(NeurIPS) Li, Z, Huang, Q, Zhu, Y, *Yang, L, Mohammadi Amiri, M, van Stein, N & van Leeuwen, M Scalable, Explainable and Provably Robust Anomaly Detection with One-Step Flow Matching. In: Proceedings of the Conference on Neural Information Processing Systems (NeurIPS 2025), 2025 (*corresponding).
(NeurIPS) Yang, L & van Leeuwen, M Conditional Density Estimation with Histogram Trees. NeurIPS 2024.
(ECML-PKDD) Yang, L & van Leeuwen, M, Truly Unordered Probabilistic Rule Sets for Multi-class Classification, ECML-PKDD 2022
(SDM) Marx, A, *Yang, L & van Leeuwen, M, Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multi-Dimensional Adaptive Histograms. In: Proceedings of the SIAM Conference on Data Mining 2021, SDM'21 (*contributed equally, alphabetical order).
Journal
Yang, L, Baratchi, M, & van Leeuwen, M, Unsupervised Discretization by Two-dimensional MDL-based Histogram, Machine Learning, Springer, 2023
Yang, L, van der Meijden, S, Arbous, S & van Leeuwen, M Interpretable Machine Learning for Identifying ICU Readmission Risk in Subgroups with Probabilistic Rules. Journal of the American Medical Informatics Association, Oxford Journals
Wang, Z, Yang, L, Ranyal, E, Briessinck, M, Eckelman, MJ, Cao, Z & Hernando, D Computational optimization for low-carbon and circular pavement management at the network level. Resources, Conservation & Recycling vol.225, Elsevier, 2026
Workshop
Yang, L & van Leeuwen, M Human-guided Rule Learning for ICU Readmission Risk Analysis. In: Proceedings of the Workshop on AI and Data Science for Healthcare (AIDSH) at KDD 2024, 2024.
Yang, L, Opdam, T & van Leeuwen, M Histogram-based Probabilistic Rule Lists for Numeric Targets. In: Proceedings of the international workshop on Knowledge Discovery in Inductive Databases (KDID 2022) at ECML PKDD 2022, 2022.
Yang, L & van Leeuwen, M, Probabilistic Rule Sets Ready for Interactive Machine Learning. In: AAAI22-Workshop on Interactive Machine Learning, 2022
Invited Talks and Community Service
Invited Talks and Community Service
Invited talk at the Algorithmics Group of the Faculty of Engineering, Mathematics and Computer Science of TU Delft on Nov 10th 2025.
Invited talk at VUML seminar at VU Amsterdam on 27 of Oct 2025, titled MDL-based Interpretable Machine Learning for High-stakes Applications.
ECML-PKDD Program Committee 2024
Invited Talks on 23 February 2023, at ECDA Insights Social AI seminar at Erasmus Centre for Data Analytics, Eramus University Rotterdam, The Netherlands
Invited journal reviewer: Data Mining and Knowledge Discovery Journal, Intelligent Data Analysis Journal
Invited conference reviewer: KDD 2021, ICLR 2023
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