# Noise Regularization for Conditional Density Estimation

@article{Rothfuss2019NoiseRF, title={Noise Regularization for Conditional Density Estimation}, author={Jonas Rothfuss and F{\'a}bio Ferreira and Simon B{\"o}hm and Simon Walther and Maxim Ulrich and Tamim Asfour and Andreas Krause}, journal={ArXiv}, year={2019}, volume={abs/1907.08982} }

Modelling statistical relationships beyond the conditional mean is crucial in many settings. Conditional density estimation (CDE) aims to learn the full conditional probability density from data. Though highly expressive, neural network based CDE models can suffer from severe over-fitting when trained with the maximum likelihood objective. Due to the inherent structure of such models, classical regularization approaches in the parameter space are rendered ineffective. To address this issue, we… CONTINUE READING

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