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Heteroscedastic Gaussian Processes and Random Features: Scalable Motion Primitives with Guarantees., , , , and . CoRL, volume 229 of Proceedings of Machine Learning Research, page 3010-3029. PMLR, (2023)Deformable surface reconstruction via Riemannian metric preservation., , and . Comput. Vis. Image Underst., (2024)White Paper 11: Artificial Intelligence, Robotics and Data Science, , , , , , , , , and 1 other author(s). (2021)Contextual Policy Search for Micro-Data Robot Motion Learning through Covariate Gaussian Process Latent Variable Models., , and . IROS, page 5511-5517. IEEE, (2020)Mutual Information Weighing for Probabilistic Movement Primitives., and . CCIA, volume 356 of Frontiers in Artificial Intelligence and Applications, page 365-368. IOS Press, (2022)Linear quadratic control of nonlinear systems with Koopman operator learning and the Nyström method., , , , , , and . CoRR, (2024)Gaussian-Process-based Robot Learning from Demonstration., , , , and . CoRR, (2020)Reward-Weighted GMM and Its Application to Action-Selection in Robotized Shoe Dressing., , , and . ROBOT (2), volume 694 of Advances in Intelligent Systems and Computing, page 141-152. Springer, (2017)Mixtures of Controlled Gaussian Processes for Dynamical Modeling of Deformable Objects., , , and . L4DC, volume 168 of Proceedings of Machine Learning Research, page 415-426. PMLR, (2022)White Paper on Artificial Intelligence, Robotics and Data Science, , , , , , , , , and 1 other author(s). (2021)