Metric-Guided Multi-task Learning

Jinfu Ren, Yang LIU, Jiming LIU*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

Abstract

Multi-task learning (MTL) aims to solve multiple related learning tasks simultaneously so that the useful information in one specific task can be utilized by other tasks in order to improve the learning performance of all tasks. Many representative MTL methods have been proposed to characterize the relationship between different learning tasks. However, the existing methods have not explicitly quantified the distance or similarity of different tasks, which is actually of great importance in modeling the task relation for MTL. In this paper, we propose a novel method called Metric-guided MTL which explicitly measures the task distance using a metric learning strategy. Specifically, we measure the distance between different tasks using their projection parameters and learn a distance metric accordingly, so that the similar tasks are close to each other while the uncorrelated tasks are faraway from each other, in terms of the learned distance metric. With a metric-guided regularizer incorporated in the proposed objective function, we open a new way to explore the related information among tasks. The proposed method can be efficiently solved via an alternative method. Experiments on both synthetic and real-world benchmark datasets demonstrate the superiority of the proposed method over existing MTL methods in terms of prediction accuracy.

Original languageEnglish
Title of host publicationFoundations of Intelligent Systems - 25th International Symposium, ISMIS 2020, Proceedings
EditorsDenis Helic, Martin Stettinger, Alexander Felfernig, Gerhard Leitner, Zbigniew W. Ras
PublisherSpringer Science and Business Media Deutschland GmbH
Pages21-31
Number of pages11
ISBN (Print)9783030594909
DOIs
Publication statusPublished - 2020
Event25th International Symposium on Methodologies for Intelligent Systems, ISMIS 2020 - Graz, Austria
Duration: 23 Sep 202025 Sep 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12117 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference25th International Symposium on Methodologies for Intelligent Systems, ISMIS 2020
Country/TerritoryAustria
CityGraz
Period23/09/2025/09/20

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Metric learning
  • Metric-guided multi-task learning
  • Multi-task learning
  • Task relation

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