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Explainable online ensemble of deep neural network pruning for time series forecasting., , and . Mach. Learn., 111 (9): 3459-3487 (2022)Explainable Quantum Machine Learning., , , , , and . CoRR, (2023)Evaluation of the Application of Smart Glasses for Decentralized Control Systems in Logistics., , , , and . ITSC, page 4470-4476. IEEE, (2019)Shapley Values with Uncertain Value Functions., , , , and . IDA, volume 13876 of Lecture Notes in Computer Science, page 156-168. Springer, (2023)SancScreen: Towards a Real-world Dataset for Evaluating Explainability Methods., , , and . LWDA, volume 3341 of CEUR Workshop Proceedings, page 33-44. CEUR-WS.org, (2022)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)Explainable Online Deep Neural Network Selection Using Adaptive Saliency Maps for Time Series Forecasting., , and . ECML/PKDD (1), volume 12975 of Lecture Notes in Computer Science, page 404-420. Springer, (2021)A Unified Framework for Assessing Energy Efficiency of Machine Learning., , , and . PKDD/ECML Workshops (1), volume 1752 of Communications in Computer and Information Science, page 39-54. Springer, (2022)Harnessing Prior Knowledge for Explainable Machine Learning: An Overview., , , , , , , , and . SaTML, page 450-463. IEEE, (2023)An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning., , , , , and . ECML/PKDD (3), volume 14171 of Lecture Notes in Computer Science, page 462-478. Springer, (2023)