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Learning latent functions for causal discovery., , , and . Mach. Learn. Sci. Technol., 4 (3): 35004 (September 2023)Disentangling Derivatives, Uncertainty and Error in Gaussian Process Models., , and . CoRR, (2020)The Kernelized Taylor Diagram., , , , , , and . NAIS, volume 1650 of Communications in Computer and Information Science, page 125-131. Springer, (2022)Information Theory in Density Destructors., , , , and . CoRR, (2020)Disentangling Derivatives, Uncertainty and Error in Gaussian Process Models., , and . IGARSS, page 4051-4054. IEEE, (2018)Kernel Methods and their derivatives: Concept and perspectives for the Earth system sciences., , , , and . CoRR, (2020)Learning Relevant Features of Optical Water Types., , , and . IEEE Geosci. Remote. Sens. Lett., (2022)Accounting for Input Noise in Gaussian Process Parameter Retrieval., , and . IEEE Geosci. Remote. Sens. Lett., 17 (3): 391-395 (2020)RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks., , , , , , , , and . CoRR, (2020)Information Theory Measures via Multidimensional Gaussianization., , , , and . CoRR, (2020)