Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings
Abstract
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.
Cite
@article{arxiv.1906.02236,
title = {Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings},
author = {Jean-Francois Ton and Lucian Chan and Yee Whye Teh and Dino Sejdinovic},
journal= {arXiv preprint arXiv:1906.02236},
year = {2021}
}