Etienne van de Bijl

Volledige naam

E.P. van de Bijl

E-mail

Etienne.van.de.Bijl@cwi.nl

Telefoon

+31 20 592 4276

Kamer

M326

Afdeling(en)

Stochastics

Functie(s)

PhD student

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, 2025. doi:10.1007/s00357-025-09510-9
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)