Online learning and online convex optimization

Shai Shalev-Shwartz*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

1277 Scopus citations

Abstract

Online learning is a well established learning paradigm which has both theoretical and practical appeals. The goal of online learning is to make a sequence of accurate predictions given knowledge of the correct answer to previous prediction tasks and possibly additional available information. Online learning has been studied in several research fields including game theory, information theory, and machine learning. It also became of great interest to practitioners due the recent emergence of large scale applications such as online advertisement placement and online web ranking. In this survey we provide a modern overview of online learning. Our goal is to give the reader a sense of some of the interesting ideas and in particular to underscore the centrality of convexity in deriving efficient online learning algorithms. We do not mean to be comprehensive but rather to give a high-level, rigorous yet easy to follow, survey.

Original languageAmerican English
Pages (from-to)107-194
Number of pages88
JournalFoundations and Trends in Machine Learning
Volume4
Issue number2
DOIs
StatePublished - 2011

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