PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Online multitask learning
Ofer Dekel, Yoram Singer and Philip Long
In: COLT 2006, 22-25 June, 2006., Pittsburgh, Pennsylvania, USA.

Abstract

We study the problem of online learning of multiple tasks in parallel. On each online round, the algorithm receives an instance and makes a prediction for each one of the parallel tasks. We consider the case where these tasks all contribute toward a common goal. We capture the relationship between the tasks by using a single global loss function to evaluate the quality of the multiple predictions made on each round. Specifically, each individual prediction is associated with its own individual loss, and then these loss values are combined using a global loss function. We present several families of online algorithms which can use any absolute norm as a global loss function. We prove worst-case relative loss bounds for all of our algorithms.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:2150
Deposited By:Ofer Dekel
Deposited On:14 July 2006