32nd Conference on Neural Information Processing Systems (NIPS)
Abstract
Structured prediction provides a general framework to deal with supervised problems where the outputs have semantically rich structure. While classical approaches
consider finite, albeit potentially huge, output spaces, in this paper we discuss how
structured prediction can be extended to a continuous scenario. Specifically, we
study a structured prediction approach to manifold valued regression. We characterize a class of problems for which the considered approach is statistically consistent
and study how geometric optimization can be used to compute the corresponding
estimator. Promising experimental results on both simulated and real data complete
our stud