Neural-Kernelized Conditional Density Estimation

Understand

Conditional density estimation is a general framework for solving various problems in machine learning.

• Among existing methods, non-parametric and/or kernel-based methods are often difficult to use on large datasets, while methods based on neural networks usually make restrictive parametric assumptions on the probability densities.
• Here, we propose a novel method for estimating the conditional density based on score matching.
• In contrast to existing methods, we employ scalable neural networks, but do not make explicit parametric assumptions on densities.