Imperial College London

DrCarloCiliberto

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 6173c.ciliberto CV

 
 
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Location

 

1003Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Rudi:2018,
author = {Rudi, A and Ciliberto, C and Marconi, GM and Rosasco, L},
publisher = {Massachusetts Institute of Technology Press},
title = {Manifold structured prediction},
url = {http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000461852000014&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202},
year = {2018}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - 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.
AU - Rudi,A
AU - Ciliberto,C
AU - Marconi,GM
AU - Rosasco,L
PB - Massachusetts Institute of Technology Press
PY - 2018///
SN - 1049-5258
TI - Manifold structured prediction
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000461852000014&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - http://hdl.handle.net/10044/1/71094
ER -