TY  - GEN
UR  - https://papers.nips.cc/paper/2018/hash/f6185f0ef02dcaec414a3171cd01c697-Abstract.html
PB  - Neural Information Processing Systems (NIPS)
N2  - 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
ID  - discovery10118772
A1  - Rudi, A
A1  - Ciliberto, C
A1  - Marconi, GM
A1  - Rosasco, L
T3  - Advances in Neural Information Processing Systems
CY  - Montreal, Canada
Y1  - 2018/12/08/
AV  - public
EP  - 12
TI  - Manifold Structured Prediction
N1  - This version is the version of record. For information on re-use, please refer to the publisher?s terms and conditions.
ER  -