In neural network-based conditional density estimation (CDE), classic regularization approaches in the parameter space are mostly ineffective. To address this issue, we develop a model-agnostic noise regularization method for CDE that adds random perturbations to the data during training. We demonstrate that the proposed approach corresponds to a smoothness regularization, we prove its asymptotic consistency and show across 7 datasets and 3 CDE models that this works well. Result: makes neural network-based CDE the preferable method over previous non- and semi-parametric approaches, even when training data is scarce!

Paper: https://arxiv.org/abs/1907.08982
Code: https://github.com/freelunchtheorem/Conditional_Density_Estimation