Etienne van de Bijl

Full Name

E.P. van de Bijl

Email

Etienne.van.de.Bijl@cwi.nl

Phone

+31 20 592 4276

Room

M326

Department(s)

Stochastics

Function(s)

PhD student - Unknown

Etienne van de Bijl

van de Bijl, E., Klein, J., Pries, J., Bhulai, S., & van der Mei, R. (2025). The Dutch Scaler Performance Indicator: How much did my model actually learn?. Journal of Classification.
van de Bijl, E., Klein, J., Pries, J., Bhulai, S., Hoogendoorn, M., & van der Mei, R. (2024). The dutch draw: Constructing a universal baseline for binary classification problems. Journal of Applied Probability, 1–19. doi:10.1017/jpr.2024.52
Pries, J., van de Bijl, E., Klein, J., Bhulai, S., & van der Mei, R. (2023). The optimal input-independent baseline for binary classification: The Dutch Draw. Statistica Neerlandica. doi:10.1111/stan.12297
van Leeuwen, B., Gansekoele, A., Pries, J., van de Bijl, E., & Klein, J. (2022). Explainable kinship: The importance of facial features in kinship recognition. In IARIA Congress 2022: The 2022 IARIA Annual Congress on Frontiers in Science, Technology, Services, and Applications (pp. 54–60).
van de Bijl, E., Klein, J., Pries, J., van der Mei, R., & Bhulai, S. (2022). Detecting Novel Variants of Application Layer (D)DoS Attacks using Supervised Learning. In IARIA Congress 2022: The 2022 IARIA Annual Congress on Frontiers in Science, Technology, Services, and Applications (pp. 25–31).
van de Bijl, E., Klein, J., Pries, J., van der Mei, R., & Bhulai, S. (2022). Detecting novel application layer cybervariants using supervised learning. International Journal on Advances in Security, 15, 75–85.

Best paper award the IARIA congress 2022 (2022)

Ontwikkeling van Data Analytics, Data Science en Crypto Analyse technieken (None)