Privacy-Preserving Generalized Linear Models using Distributed Block Coordinate Descent. - Erik-Jan van Kesteren; Chang Sun 001; Daniel L. Oberski; Michel Dumontier; Lianne Ippel (CoRR, 2019)

Fair inference on error-prone outcomes. - Laura Boeschoten; Erik-Jan van Kesteren; Ayoub Bagheri; Daniel L. Oberski (CoRR, 2020)

Achieving Fair Inference Using Error-Prone Outcomes. - Laura Boeschoten; Erik-Jan van Kesteren; Ayoub Bagheri; Daniel L. Oberski (Int. J. Interact. Multim. Artif. Intell., 2021)

Structural Equations with Latent Variables: Computational Solutions for Modern Data Problems. - Erik-Jan van Kesteren (2021)

On Text-based Personality Computing: Challenges and Future Directions. - Qixiang Fang; Anastasia Giachanou; Ayoub Bagheri; Laura Boeschoten; Erik-Jan van Kesteren; Mahdi Shafiee Kamalabad; Daniel L. Oberski (ACL (Findings), 2023)

A density ratio framework for evaluating the utility of synthetic data. - Thom Benjamin Volker; Peter-Paul de Wolf; Erik-Jan van Kesteren (CoRR, 2024)

Open data work for empowered deliberative democracy: Findings from a living lab study. - Erna H. J. M. Ruijer; Carmen Dymanus; Erik-Jan van Kesteren; Laura Boeschoten; Albert Meijer 001 (Gov. Inf. Q., 2024)

To democratize research with sensitive data, we should make synthetic data more accessible. - Erik-Jan van Kesteren (Patterns, 2024)

Metasyn: Transparent Generation of Synthetic Tabular Data with Privacy Guarantees. - Raoul Schram; Samuel Spithorst; Erik-Jan van Kesteren (J. Open Source Softw., 2025)

Community Detection in bipartite Signed Networks is Highly dependent on parameter Choice. - Elena Candellone; Erik-Jan van Kesteren; Sofia Chelmi; Javier Garcia-Bernardo (Adv. Complex Syst., 2025)