Bertrand Lebichot

I am a post-doc researcher in data mining and machine learning. My research interest are deep learning, graph mining, and anomaly detection on concrete Fintech case studies. I am always searching for new challenges.

I hold a Master of Engineering. I am currently a post-doc researcher at Université du Luxembourg and part-time lecturer at UCL. I am fluent in French and English. I also speak Dutch and German.

Research fields

  • Network-based data mining

    - Network-based classification
    - Network/node criticality
    - Markov decision processes

  • Fraud detection

    - Network-based fraud detection
    - Data-driven fraud detection systems

  • Transfer learning

    - Domain adaptation
    - Multitask learning
    - Biomedical classification

  • Churn prediction

    - Data-based marketing campaign
    - Causal inference
    - Explainability

  • Deep learning

    - GPU computing
    - Classification
    - Deep graph mining

Teaching

As teaching assistant

LECGE1215 Informatique en économie et gestion LINK
LINGE1225 Algorithmique et programmation en économie et gestion LINK
LSINF1250 Mathématique pour l’informatique LINK
LLSMF2013 Analyse de données quantitatives LINK
LSINF2275 Data mining and decision making LINK

As professor

MLSMM2154 Machine Learning LINK
MLSMM2151 Data Mining LINK
MQANT1109 Informatique de gestion LINK

Publications

PhD Thesis

2018 Network analysis based on bag-of-paths: classification, node criticality and randomized policies PDF

Per year

2014 B. Lebichot, K. Francoisse, I. Kivimaki and M. Saerens
Semi-Supervised Classification through the Bag-of-Paths Group Betweenness
In IEEE Transactions on Neural Networks and Learning Systems volume 25 (6 2014), pp. 1173–1186. 		PDF
2016 I. Kivimaki, B. Lebichot, J. Saramaki and M. Saerens
Two Betweenness Centrality Measures based on Randomized Shortest Paths
In Scientific Reports. Volume 6, Article number: 19668 (2016). 		PDF
2016 B. Lebichot, F. Braun, O. Caelens and M. Saerens
A Graph-Based, Semi-Supervised, Credit Card Fraud Detection System
In Complex Networks & Their Applications V. COMPLEX NETWORKS 2016. Studies in Computational Intelligence, vol 693. Springer, Cham. 		PDF
2017 F. Braun, O. Caelen, E. Smirnov, S. Kelk and B. Lebichot
Improving Card Fraud Detection through Suspicious Pattern Discovery
In Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science, vol 10351. Springer, Cham. 		LINK
2018 B. Lebichot and M. Saerens
A Bag-of-Paths Node Criticality Measure
In Neurocomputing. Volume 275 (January 2018), pp. 224–236 		PDF
2019 B. Lebichot, Y-A. Le Brogne, L. He-Guelton, F. Oblé, G. Bontempi
Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection
In Recent Advances in Big Data and Deep Learning, pp. 78–88. Springer, New York (2019) 		PDF
2019 T. Verhelst, O. Caelen, J-C. Dewitte, B. Lebichot and G. Bontempi
Understanding telecom customer churn with machine learning: from prediction to causal inference
In In Artificial Intelligence and Machine Learning (pp. 182-200). Springer, Cham. 		PDF
2019 S. Courtain, B. Lebichot and M. Saerens
Graph-based fraud detection with the free energy distance
In International Conference on Complex Networks and Their Applications (pp. 40-52). Springer, Cham. 		PDF
2019 T. Verhelst, O. Caelen, J-C. Dewitte, B. Lebichot and G. Bontempi
Understanding telecom customer churn with machine learning: from prediction to causal inference
In Artificial Intelligence and Machine Learning : 31st Benelux AI Conference, BNAIC 2019, and 28th Belgian-Dutch Machine Learning Conference, BENELEARN 2019, Brussels, Belgium, November 6-8, 2019, Revised Selected Papers (Vol. 1196, p. 182). Springer Nature. 		PDF
2020 B. Lebichot and M. Saerens
An experimental study of graph-based semisupervised classification with additional node information
In Knowledge and Information Systems, 62(11), 4337-4371. 		PDF
2021 Buroni, G., Lebichot, B., & Bontempi, G.
AST-MTL : An Attention-based Multi- Task Learning Strategy for Traffic Forecasting.
In EEE access, volume 9, pp. 77359- 77370. 		PDF
2021 Lebichot, B., Paldino, G. M., Siblini, W., He-Guelton, L., Oblé, F., & Bontempi, G.
Incremental learning strategies for credit cards fraud detection.
In International Journal of Data Science and Analytics, 1-10. 		PDF
2021 Lothritz, C., Allix, K., Lebichot, B., Veiber, L., Bissyandé, T. F., & Klein, J.
Comparing MultiLingual and Multiple MonoLingual Models for Intent Classification and Slot Filling.
In International Conference on Applications of Natural Language to Information Systems (pp. 367-375). Springer, Cham. 		PDF
2021 W. Siblini, G. Coter, R. Fabry, L. He-Guelton, F. Oblé, B. Lebichot, Y.-A. Le Borgne, G. Bontempi
Transfer learning for credit card fraud detection : A journey from research to production.
In In The Proceedings of the Data Science and Advanced Analytics (DSAA 2021) IEEE conference (In Press). 		PDF
2021 Lebichot, B., Verhelst, T., Le Borgne, Y. A., He-Guelton, L., Oblé, F., & Bontempi, G. (2021).
Transfer Learning Strategies for Credit Card Fraud Detection.
In IEEE access, 9, 114754-114766. 		PDF