Joris Pries

Full Name

J. Pries

Email

Joris.Pries@cwi.nl

Phone

+31 20 592 4301

Room

M337

Department(s)

Stochastics

Function(s)

PhD student

Joris Pries

Pries, J., Bhulai, S., & van der Mei, R. (2023). Active pairwise distance learning for efficient labeling of large datasets by human experts. Applied Intelligence. doi:10.1007/s10489-023-04516-5
Pries, J. (2023, June 26). Fundamentals of data science : a new outlook.
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
Berkelmans, G., Bhulai, S., van der Mei, R., & Pries, J. (2023). The Berkelmans–Pries dependency function: A generic measure of dependence between random variables. Journal of Applied Probability, 60(4), 1115–1135. doi:10.1017/jpr.2022.118
Pries, J., Bhulai, S., & van der Mei, R. (2022). Evaluating a face generator from a human perspective. Machine Learning with Applications, 10, 100412:1–100412:12. doi:10.1016/j.mlwa.2022.100412
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.