Exploiting compositionality to explore a large space of model structures

Understand

The recent proliferation of richly structured probabilistic models raises the question of how to automatically determine an appropriate model for a dataset.

• We investigate this question for a space of matrix decomposition models which can express a variety of widely used models from unsupervised learning.
• To enable model selection, we organize these models into a context-free grammar which generates a wide variety of structures through the compositional application of a few simple rules.
• We use our grammar to generically and efficiently infer latent components and estimate predictive likelihood for nearly 2500 structures using a small toolbox of reusable algorithms.