NIPS Proceedingsβ

Manifold Structured Prediction

Part of: Advances in Neural Information Processing Systems 31 (NIPS 2018) pre-proceedings

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Conference Event Type: Poster


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 study.