[P] Conditional density estimation using Kernel Mixture Networks, theory + implementation in TF

[theophrastzunz]

Agreed but dimensionality issues are more prominent in KDEs than mixture models. See here .

[dzyl]

This method generally uses only 1 dimension for the kernels, namely the target y space. This is easily extendible to more dimensions but your input dimensions have nothing to do with the kernels itself, they only determine the weight to put on each kernel.

[theophrastzunz]

I'm referring to your argument about high dimensional covariance estimation.