  Current meta-learning approaches focus on learning functional representations
of relationships between variables, i.e. on estimating conditional expectations
in regression. In many applications, however, we are faced with conditional
distributions which cannot be meaningfully summarized using expectation only
(due to e.g. multimodality). Hence, we consider the problem of conditional
density estimation in the meta-learning setting. We introduce a novel technique
for meta-learning which combines neural representation and noise-contrastive
estimation with the established literature of conditional mean embeddings into
reproducing kernel Hilbert spaces. The method is validated on synthetic and
real-world problems, demonstrating the utility of sharing learned
representations across multiple conditional density estimation tasks.
