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

*Proceedings of The 24th International Conference on Artificial Intelligence and Statistics*, PMLR 130:1099-1107, 2021.

#### Abstract

Current meta-learning approaches focus on learning functional representations of relationships between variables, \textit{i.e.} estimating conditional expectations in regression. In many applications, however, the conditional distributions cannot be meaningfully summarized solely by expectation (due to \textit{e.g.} multimodality). We introduce a novel technique for meta-learning conditional densities, which combines neural representation and noise contrastive estimation together with well-established literature in conditional mean embeddings into reproducing kernel Hilbert spaces. The method shows significant improvements over standard density estimation methods on synthetic and real-world data, by leveraging shared representations across multiple conditional density estimation tasks.