2023
[17-06] I was fortunate to do a research stay at the van der Schaar Lab at the University of Cambridge. The resulting work will be presented at ICML 2023 in Honolulu, Hawaii! Check out our paper: Vanderschueren, T.*, Curth, A.*, Verbeke, W., & van der Schaar, M. (2023). Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time. International Conference on Machine Learning..
[20-04] Our paper on optimizing maintenance with causal machine learning is now published in the International Journal of Production Economics.
[24-03] I wrote a blog post on our work on maintenance for Data Science Briefings, the DataMiningApps newsletter.
2022
[08-06] Our new paper “Prescriptive maintenance with causal machine learning” is now on arXiv, check it out! Vanderschueren, T., Boute, R., Baesens, B., Verdonck, T., & Verbeke, W. (2022). Prescriptive maintenance with causal machine learning. arXiv preprint arXiv:2206.01562.
[07-06] Very happy to announce our new Leuven.AI story: Minimizing fraud losses using cost-sensitive machine learning and learning to rank! This blog post gives a high level introduction to some of the work I’ve done in fraud detection. It’s related to the following two papers:
- Vanderschueren, T., Verdonck, T., Baesens, B., & Verbeke, W. (2022). Predict-then-optimize or predict-and-optimize? An empirical evaluation of cost-sensitive learning strategies. Information Sciences, 594, 400-415. https://doi.org/10.1016/j.ins.2022.02.021
- Vanderschueren, T., Baesens, B., Verdonck, T., & Verbeke, W. (2022). A new perspective on classification: optimally allocating limited resources to uncertain tasks. arXiv preprint arXiv:2202.04369.