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Erik-Jan van Kesteren
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2020 – today
- 2025
- [j5]Elena Candellone
, Erik-Jan van Kesteren
Community Detection in bipartite Signed Networks is Highly dependent on parameter Choice. Adv. Complex Syst. 28(3): 2540002:1-2540002:28 (2025) - [j4]Raoul Schram
Metasyn: Transparent Generation of Synthetic Tabular Data with Privacy Guarantees. J. Open Source Softw. 10(105): 7099 (2025) - 2024
- [j3]Erna H. J. M. Ruijer, Carmen Dymanus, Erik-Jan van Kesteren
Open data work for empowered deliberative democracy: Findings from a living lab study. Gov. Inf. Q. 41(1): 101902 (2024) - [j2]Erik-Jan van Kesteren
To democratize research with sensitive data, we should make synthetic data more accessible. Patterns 5(10): 101049 (2024) - [i6]Erik-Jan van Kesteren:
To democratize research with sensitive data, we should make synthetic data more accessible. CoRR abs/2404.17271 (2024) - [i5]Elena Candellone, Erik-Jan van Kesteren, Sofia Chelmi, Javier Garcia-Bernardo:
Community detection in bipartite signed networks is highly dependent on parameter choice. CoRR abs/2405.08203 (2024) - [i4]Thom Benjamin Volker, Peter-Paul de Wolf, Erik-Jan van Kesteren:
A density ratio framework for evaluating the utility of synthetic data. CoRR abs/2408.13167 (2024) - 2023
- [c1]Qixiang Fang
On Text-based Personality Computing: Challenges and Future Directions. ACL (Findings) 2023: 10861-10879 - 2022
- [i3]Qixiang Fang, Anastasia Giachanou, Ayoub Bagheri, Laura Boeschoten, Erik-Jan van Kesteren, Mahdi Shafiee Kamalabad, Daniel L. Oberski:
On Text-based Personality Computing: Challenges and Future Directions. CoRR abs/2212.06711 (2022) - 2021
- [b1]Erik-Jan van Kesteren:
Structural Equations with Latent Variables: Computational Solutions for Modern Data Problems. Utrecht University, Netherlands, 2021 - [j1]Laura Boeschoten, Erik-Jan van Kesteren, Ayoub Bagheri, Daniel L. Oberski:
Achieving Fair Inference Using Error-Prone Outcomes. Int. J. Interact. Multim. Artif. Intell. 6(5): 9-15 (2021) - 2020
- [i2]Laura Boeschoten, Erik-Jan van Kesteren, Ayoub Bagheri, Daniel L. Oberski:
Fair inference on error-prone outcomes. CoRR abs/2003.07621 (2020)
2010 – 2019
- 2019
- [i1]Erik-Jan van Kesteren, Chang Sun, Daniel L. Oberski, Michel Dumontier, Lianne Ippel:
Privacy-Preserving Generalized Linear Models using Distributed Block Coordinate Descent. CoRR abs/1911.03183 (2019)
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last updated on 2025-04-09 21:27 CEST by the dblp team
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