Practical effectiveness of density estimation for supervised tasks

Determine whether unsupervised density-estimation methods provide a practical approach for solving supervised learning tasks, rather than merely offering theoretical connections.

Background

The paper presents learning the probability density function, its logarithm, or its score function as a fundamental goal of unsupervised learning. Although the learned density can theoretically support classification and regression through density ratios and conditional densities, the paper notes that these connections may not establish practical usefulness for supervised learning. The unresolved issue is therefore whether density estimation methods yield an effective practical solution for supervised tasks.