Flow Models for Arbitrary Conditional Likelihoods

Understand

Understanding the dependencies among features of a dataset is at the core of most unsupervised learning tasks.

• However, a majority of generative modeling approaches are focused solely on the joint distribution $p(x)$ and utilize models where it is intractable to obtain the conditional distribution of some arbitrary subset of features $x_u$ given the rest of the observed covariates $x_o$: $p(x_u \mid x_o)$.
• Traditional conditional approaches provide a model for a fixed set of covariates conditioned on another fixed set of observed covariates.
• Instead, in this work we develop a model that is capable of yielding all conditional distributions $p(x_u \mid x_o)$ (for arbitrary $x_u$) via tractable conditional likelihoods.