Publications

Published or Accepted Journal Papers

1. D. Cacciarelli and M. Kulahci (2024). Semi-supervised learning for predictive modeling in industrial applications. Quality Engineering. https://doi.org/10.1080/08982112.2024.2440371

2. D. Cacciarelli and M. Kulahci (2024). Active learning for industrial applications. Quality Engineering. http://dx.doi.org/10.1080/08982112.2024.2402376

3. D. Cacciarelli, M. Kulahci and J.S. Tyssedal (2024). Real-time sampling strategies for regression with irrelevant features. International Journal of Semantic Computing. doi.org/10.1142/S1793351X24430013

4. D. Cacciarelli and M. Kulahci (2023). Active learning for data streams: a survey. Machine Learning. doi.org/10.1007/s10994-023-06454-2

5. D. Cacciarelli and M. Kulahci (2023). Hidden dimensions of the data: PCA vs autoencoders. Quality Engineering. doi.org/10.1080/08982112.2023.2231064

6. D. Cacciarelli, M. Kulahci and J.S. Tyssedal (2023). Robust online active learning. Quality and Reliability Engineering International. doi.org/10.1002/qre.3392

7. D. Cacciarelli, M. Kulahci and J.S. Tyssedal (2022). Stream-based Active Learning with Linear Models. Knowledge-Based Systems. doi.org/10.1016/j.knosys.2022.109664

8. D. Cacciarelli and M. Kulahci (2022). A novel fault detection and diagnosis approach based on orthogonal autoencoders. Computers & Chemical Engineering. doi.org/10.1016/j.compchemeng.2022.107853 

9. D. Cacciarelli and M. Boresta (2021). What drives a donor? A machine learning-based approach for predicting responses of nonprofit direct marketing campaigns. Journal of Philanthropy and Marketing. doi.org/10.1002/nvsm.1724 

Conference Proceedings and Workshop Papers

1. D. Cacciarelli, M. Kulahci and J.S. Tyssedal (2023). Stream-based active learning for regression with dynamic feature selection. IEEE International Conference on Transdisciplinary AI (TransAI). doi.org/10.1109/TransAI60598.2023.00030

2. D. Manjah, D. Cacciarelli, B. Standaert, M. Benkedadra, G. Rotsart, S. Galland, B. Macq and C. De Vleeschouwer (2023). Stream-Based Active Distillation for Scalable Model Deployment. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. doi.org/10.1109/CVPRW59228.2023.00528

3. D. Cacciarelli, M. Kulahci and J.S. Tyssedal (2022).  Online Active Learning for Soft Sensor Development using Semi-Supervised Autoencoders. International Conference on Machine Learning (ICML) Workshop on Adaptive Experimental Design and Active Learning in the Real World. arXiv:2212.13067