Local Rademacher Complexity-Based Learning Guarantees For Multi-Task Learning

Keywords

Excess Risk Bounds; Local Rademacher Complexity; Multi-task Learning

Abstract

We show a Talagrand-type concentration inequality for Multi-Task Learning (MTL), with which we establish sharp excess risk bounds for MTL in terms of the Local Rademacher Complexity (LRC). We also give a new bound on the LRC for any norm regularized hypothesis classes, which applies not only to MTL, but also to the standard Single-Task Learning (STL) setting. By combining both results, one can easily derive fast-rate bounds on the excess risk for many prominent MTL methods, including-as we demonstrate-Schatten norm, group norm, and graph regularized MTL. The derived bounds reflect a relationship akin to a conservation law of asymptotic convergence rates. When compared to the rates obtained via a traditional, global Rademacher analysis, this very relationship allows for trading off slower rates with respect to the number of tasks for faster rates with respect to the number of available samples per task.

Publication Date

8-1-2018

Publication Title

Journal of Machine Learning Research

Volume

19

Document Type

Article

Personal Identifier

scopus

Socpus ID

85053376688 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85053376688

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