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

Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings