NeurIPS 2018poster24 citations
Manifold Structured Prediction
Alessandro Rudi, Carlo Ciliberto, GianMaria Marconi, Lorenzo Rosasco
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 study.
BibTeX
@inproceedings{NEURIPS2018_f6185f0e,
author = {Rudi, Alessandro and Ciliberto, Carlo and Marconi, GianMaria and Rosasco, Lorenzo},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Manifold Structured Prediction},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/f6185f0ef02dcaec414a3171cd01c697-Paper.pdf},
volume = {31},
year = {2018}
}