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Active Sampling for Learning Interpretable Surrogate Machine Learning Models., and . DSAA, page 264-272. IEEE, (2020)An Actor-Critic Ensemble Aggregation Model for Time-Series Forecasting., , and . ICDE, page 2255-2260. IEEE, (2021)Online Adaptive Multivariate Time Series Forecasting., , and . ECML/PKDD (6), volume 13718 of Lecture Notes in Computer Science, page 19-35. Springer, (2022)Online Explainable Forecasting using Regions of Competence., and . TempXAI@PKDD/ECML, volume 3761 of CEUR Workshop Proceedings, page 7-11. CEUR-WS.org, (2024)A Drift-Based Dynamic Ensemble Members Selection Using Clustering for Time Series Forecasting., , and . ECML/PKDD (1), volume 11906 of Lecture Notes in Computer Science, page 678-694. Springer, (2019)Explainable Adaptive Tree-based Model Selection for Time-Series Forecasting., and . ICDM, page 180-189. IEEE, (2023)Real-time prediction of process forces in milling operations using synchronized data fusion of simulation and sensor data., , , and . Eng. Appl. Artif. Intell., (2020)Meta-Adversarial Training of Neural Networks for Binary Classification., and . IJCNN, page 1-7. IEEE, (2021)Learning Ensembles in the Presence of Imbalanced Classes., , , , and . ICPRAM, page 866-873. SciTePress, (2019)Online Deep Hybrid Ensemble Learning for Time Series Forecasting., and . ECML/PKDD (5), volume 14173 of Lecture Notes in Computer Science, page 156-171. Springer, (2023)