Covariate Shift in Hilbert Space: A Solution via Sorrogate Kernels

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Joint distribution optimal transportation for domain adaptation (2017, methodology): “We compare our method with four other methods: the surrogate kernel approach ([4], denoted SurK), subspace adaptation for its simplicity and good performances on visual adaptation ([8], SA), Adaptation Regularization based Transfer Learning ([30], ARTL), and the two variants of regularized optimal t”
Transfer Learning with Label Noise (2017, methodology): “For example, in the indoor WiFi localization problem [26], the signal distributions received by diﬀerent phone models vary.”
Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction (2016, methodology): “a RKHS, with a recent extension [31] developing surrogate-k ernel based kernel matching.”
Domain Adaptation with Regularized Optimal Transport (2014, methodology): “An approach combining the two logics is found in [3], where authors extend the sample re-weighing reasoning to similarity of the distributions in the feature space by the use of surrogate kernels.”