Articles dans des revues avec comité de lecture (7)
  1. 1. Nachtegael, C., De Stefani, J., & Lenaerts, T. (2023). A study of deep active learning methods to reduce labelling efforts in biomedical relation extraction. PloS one, 18(12), e0292356. doi:10.1371/journal.pone.0292356
  2. 2. Paldino, G. M., De Caro, F., De Stefani, J., Vaccaro, A. A., Villacci, D. D., & Bontempi, G. (2022). A Digital Twin Approach for Improving Estimation Accuracy in Dynamic Thermal Rating of Transmission Lines. Energies, 15(6), 2254. doi:10.3390/en15062254
  3. 3. Paldino, G. M., De Stefani, J., De Caro, F., & Bontempi, G. (2021). Does AutoML Outperform Naive Forecasting? †. Engineering Proceedings, 5(1), 36. doi:10.3390/engproc2021005036
  4. 4. De Stefani, J., & Bontempi, G. (2021). Factor-Based Framework for Multivariate and Multi-step-ahead Forecasting of Large Scale Time Series. Frontiers in Big Data, 4. doi:10.3389/fdata.2021.690267
  5. 5. De Caro, F., De Stefani, J., Bontempi, G., Vaccaro, A. A., & Villacci, D. D. (2020). Robust Assessment of Short-Term Wind Power Forecasting Models on Multiple Time Horizons. Technology and Economics of Smart Grids and Sustainable Energy, 5(1), 19. doi:10.1007/s40866-020-00090-8
  6. 6. De Caro, F., De Stefani, J., Bontempi, G., Vaccaro, A. A., & Villacci, D. D. (2020). Robust Assessment of Short-Term Wind Power Forecasting Models on Multiple Time Horizons. Technology and Economics of Smart Grids and Sustainable Energy, 5(1). doi:10.1007/s40866-020-00090-8
  7. 7. De Stefani, J., Le Borgne, Y.-A., Caelen, O., Hattab, D., & Bontempi, G. (2018). Batch and incremental dynamic factor machine learning for multivariate and multi-step-ahead forecasting. International journal of data science and analytics (Print), 7(4), 311-329. doi:10.1007/s41060-018-0150-x
    8.   Communications publiées lors de congrès ou colloques nationaux et internationaux (4)
  8. 1. Nachtegael, C., De Stefani, J., & Lenaerts, T. (2023). ALAMBIC: Active Learning Automation with Methods to Battle Inefficient Curation. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations (17 ed., pp. 117--127) Association for Computational Linguistics.
  9. 2. De Stefani, J., Caelen, O., Hattab, D., Le Borgne, Y.-A., & Bontempi, G. (2019). A Multivariate and Multi-step Ahead Machine Learning Approach to Traditional and Cryptocurrencies Volatility Forecasting. In ECML PKDD 2018 Workshops. (Lecture Notes in Computer Science, 11054, 11054). Springer. doi:10.1007/978-3-030-13463-1_1
  10. 3. Bontempi, G., Le Borgne, Y.-A., & De Stefani, J. (2017). A Dynamic Factor Machine Learning Method for Multi-variate and Multi-step-Ahead Forecasting. 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA) (pp. 222-231). doi:10.1109/DSAA.2017.1
  11. 4. De Stefani, J., Caelen, O., Hattab, D., & Bontempi, G. (2017). Machine Learning for Multi-step Ahead Forecasting of Volatility Proxies. 2nd Workshop on MIning DAta for financial applicationS (MIDAS). Vol. 1941 (pp. 17-28) MIDAS 2017(Skopje, Macedonia).
    12.   Participations à des congrès et colloques internationaux (1)
  12. 1. Nachtegael, C., De Stefani, J., & Lenaerts, T. (2023). ALAMBIC : Active Learning Automation Methods to Battle Inefficient Curation. Poster présenté à la conférence European Chapter of the Association for Computational Linguistics: System Demonstrations (17: Dubrovnik).
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