Articles dans des revues avec comité de lecture (4)
  1. 1. Verhelst, T., Mercier, D., Shrestha, J., & Bontempi, G. (2023). Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment. Machine learning. doi:10.1007/s10994-023-06317-w
  2. 2. Lebichot, B., Verhelst, T., Le Borgne, Y.-A., He-Guelton, L., Oblé, F., & Bontempi, G. (2021). Transfer Learning Strategies for Credit Card Fraud Detection. IEEE access, 9, 114754-114766. doi:10.1109/ACCESS.2021.3104472
  3. 3. Verhelst, T., Shrestha, J., Mercier, D., Dewitte, J. C., & Bontempi, G. (2021). Predicting Reach to Find Persuadable Customers: Improving Uplift Models for Churn Prevention. Lecture notes in computer science, 12986 LNAI, 44-54. doi:10.1007/978-3-030-88942-5_4
  4. 4. Verhelst, T., Caelen, O., Dewitte, J. C., Lebichot, B., & Bontempi, G. (2019). Understanding telecom customer churn with machine learning: From prediction to causal inference. CEUR Workshop Proceedings, 2491.
    5.   Thèses et mémoires (1)
  5. 1. Verhelst, T. (2024). Causal and predictive modeling of customer churn: Lessons learned from empirical and theoretical research (Thèse doctorale non-publiée). Université libre de Bruxelles, Faculté des Sciences – Informatique, Bruxelles.