  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. Nevertheless, current hyperparameter tuning methods
predominantly rely on expensive cross validation or heuristics that is not
optimized for the inference task. For conditional kernel mean embeddings with
categorical targets and arbitrary inputs, we propose a hyperparameter learning
framework based on Rademacher complexity bounds to prevent overfitting by
balancing data fit against model complexity. Our approach only requires batch
updates, allowing scalable kernel hyperparameter tuning without invoking kernel
approximations. Experiments demonstrate that our learning framework outperforms
competing methods, and can be further extended to incorporate and learn deep
neural network weights to improve generalization.
