Arslan, Y. (2023). On the Integration of Interpretable Machine Learning Techniques to Machine Learning Pipeline [Doctoral thesis, Unilu - University of Luxembourg]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/55513 |
Arslan, Y., Lebichot, B., Allix, K., Veiber, L., Lefebvre, C., Boytsov, A., Goujon, A., Bissyande, T. F. D. A., & Klein, J. (2022). Towards Refined Classifications Driven by SHAP Explanations. In A. Holzinger, P. Kieseberg, A. M. Tjoa, ... E. Weippl (Eds.), Machine Learning and Knowledge Extraction (pp. 68-81). Springer. Peer reviewed |
Arslan, Y., Lebichot, B., Allix, K., Veiber, L., Lefebvre, C., Boytsov, A., Goujon, A., Bissyande, T. F. D. A., & Klein, J. (2022). On the Suitability of SHAP Explanations for Refining Classifications. In In Proceedings of the 14th International Conference on Agents and Artificial Intelligence (ICAART 2022). doi:10.5220/0010827700003116 Peer reviewed |
Arslan, Y., Allix, K., Lefebvre, C., Boytsov, A., Bissyande, T. F. D. A., & Klein, J. (2022). Exploiting Prototypical Explanations for Undersampling Imbalanced Datasets. In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 1449-1454). doi:10.1109/ICMLA55696.2022.00228 Peer reviewed |
Arslan, Y., Allix, K., Veiber, L., Lothritz, C., Bissyande, T. F. D. A., Klein, J., & Goujon, A. (2021). A Comparison of Pre-Trained Language Models for Multi-Class Text Classification in the Financial Domain. In Companion Proceedings of the Web Conference 2021 (WWW '21 Companion), April 19--23, 2021, Ljubljana, Slovenia (pp. 260–268). New York, United States: Association for Computing Machinery. doi:10.1145/3442442.3451375 Peer reviewed |
Veiber, L., Allix, K., Arslan, Y., Bissyande, T. F. D. A., & Klein, J. (2020). Challenges Towards Production-Ready Explainable Machine Learning. In L. Veiber, K. Allix, Y. Arslan, T. F. D. A. Bissyande, ... J. Klein, Proceedings of the 2020 USENIX Conference on Operational Machine Learning (OpML 20). USENIX Association. Peer reviewed |