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 |
LOTHRITZ, C., LEBICHOT, B., ALLIX, K., VEIBER, L., BISSYANDE, T. F. D. A., KLEIN, J., Boytsov, A., Goujon, A., & Lefebvre, C. (2022). LuxemBERT: Simple and Practical Data Augmentation in Language Model Pre-Training for Luxembourgish. In Proceedings of the Language Resources and Evaluation Conference, 2022 (pp. 5080-5089). 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 |
LOTHRITZ, C., ALLIX, K., LEBICHOT, B., VEIBER, L., BISSYANDE, T. F. D. A., & KLEIN, J. (2021). Comparing MultiLingual and Multiple MonoLingual Models for Intent Classification and Slot Filling. In 26th International Conference on Applications of Natural Language to Information Systems (pp. 367-375). Springer. doi:10.1007/978-3-030-80599-9_32 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 |
LOTHRITZ, C., ALLIX, K., VEIBER, L., KLEIN, J., & BISSYANDE, T. F. D. A. (2020). Evaluating Pretrained Transformer-based Models on the Task of Fine-Grained Named Entity Recognition. In Proceedings of the 28th International Conference on Computational Linguistics (pp. 3750–3760). Peer reviewed |
Mossong, J., MOMBAERTS, L., VEIBER, L., Pastore, J., LeCoroller, G., Schnell, M., Masi, S., Huiart, L., & WILMES, P. (2020). SARS-CoV-2 Transmission in Educational Settings During an Early Summer Epidemic Wave in Luxembourg. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/44569. doi:10.2139 |
GHAMIZI, S., RWEMALIKA, R., CORDY, M., VEIBER, L., BISSYANDE, T. F. D. A., PAPADAKIS, M., KLEIN, J., & LE TRAON, Y. (2020). Data-driven simulation and optimization for covid-19 exit strategies. In S. GHAMIZI, R. RWEMALIKA, M. CORDY, L. VEIBER, T. F. D. A. BISSYANDE, M. PAPADAKIS, J. KLEIN, ... Y. LE TRAON, Data-driven simulation and optimization for covid-19 exit strategies (pp. 3434-3442). New York, NY, United States: Association for Computing Machinery. doi:10.1145/3394486.3412863 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 |