Hyperparameter Learning for Conditional Kernel Mean Embeddings with Rademacher Complexity Bounds
Conditional kernel mean embeddings are nonparametric models that encode conditional expectations in a reproducing kernel Hilbert space. While they provide a flexible and powerful framework for probabilistic inference, their performance is highly dependent on the choice of kernel and regularization hyperparameters... (read more)
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