On Structured Output Training: Hard Cases and an Efficient Alternative
We consider a class of structured prediction problems for which the assumptions made by state-of-the-art algorithms fail. To deal with exponentially sized output sets, these algorithms assume, for instance, that the best output for a given input can be found efﬁciently. While this holds for many important real world problems, there are also many relevant and seemingly simple problems where these assumptions do not hold. In this paper, we consider route prediction, which is the problem of ﬁnding a cyclic permutation of some points of interest, as an example and show that state-of-the-art approaches cannot guarantee polynomial runtime for this output set. We then present a novel formulation of the learning problem that can be trained efﬁciently whenever a particular ‘super-structure counting’ problem can be solved efﬁciently for the output set. We also list several output sets for which this assumption holds and report experimental results.