More data means less inference: A pseudo-max approach to structured learning

Abstract

The problem of learning to predict structured labels is of key importance in many applications. However, for general graph structure both learning and inference are intractable. Here we show that it is possible to circumvent this difficulty when the distribution of training examples is rich enough, via a method similar in spirit to pseudo-likelihood. We show that our new method achieves consistency, and illustrate empirically that it indeed approaches the performance of exact methods when sufficiently large training sets are used.