eprintid: 10077127
rev_number: 22
eprint_status: archive
userid: 608
dir: disk0/10/07/71/27
datestamp: 2019-09-05 19:00:20
lastmod: 2021-11-23 23:31:13
status_changed: 2019-09-05 19:00:20
type: article
metadata_visibility: show
creators_name: Luise, G
creators_name: Stamos, D
creators_name: Pontil, M
creators_name: Ciliberto, C
title: Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
note: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: We study the interplay between surrogate methods for structured prediction and techniques from multitask learning designed to leverage relationships between surrogate outputs. We propose an efficient algorithm based on trace norm regularization which, differently from previous methods, does not require explicit knowledge of the coding/decoding functions of the surrogate framework. As a result, our algorithm can be applied to the broad class of problems in which the surrogate space is large or even infinite dimensional. We study excess risk bounds for trace norm regularized structured prediction, implying the consistency and learning rates for our estimator. We also identify relevant regimes in which our approach can enjoy better generalization performance than previous methods. Numerical experiments on ranking problems indicate that enforcing low-rank relations among surrogate outputs may indeed provide a significant advantage in practice.
date: 2019
official_url: http://proceedings.mlr.press/v97/luise19a.html
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1640591
lyricists_name: Ciliberto, Carlo
lyricists_name: Luise, Giulia
lyricists_name: Pontil, Massimiliano
lyricists_id: CCILI30
lyricists_id: GLUIS61
lyricists_id: MPONT27
actors_name: Pontil, Massimiliano
actors_id: MPONT27
actors_role: owner
full_text_status: public
publication: Proceedings of the 36th International Conference on Machine Learning
volume: 97
pagerange: 4193-4202
citation:        Luise, G;    Stamos, D;    Pontil, M;    Ciliberto, C;      (2019)    Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction.                   Proceedings of the 36th International Conference on Machine Learning , 97    pp. 4193-4202.          Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10077127/1/luise19a.pdf