Data-driven online to batch conversions

Ofer Dekel, Yoram Singer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations


Online learning algorithms are typically fast, memory efficient, and simple to implement. However, many common learning problems fit more naturally in the batch learning setting. The power of online learning algorithms can be exploited in batch settings by using online-to-batch conversions techniques which build a new batch algorithm from an existing online algorithm. We first give a unified overview of three existing online-to-batch conversion techniques which do not use training data in the conversion process. We then build upon these data-independent conversions to derive and analyze data-driven conversions. Our conversions find hypotheses with a small risk by explicitly minimizing data-dependent generalization bounds. We experimentally demonstrate the usefulness of our approach and in particular show that the data-driven conversions consistently outperform the data-independent conversions.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 18 - Proceedings of the 2005 Conference
Number of pages8
StatePublished - 2005
Event2005 Annual Conference on Neural Information Processing Systems, NIPS 2005 - Vancouver, BC, Canada
Duration: Dec 5 2005Dec 8 2005

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258


Other2005 Annual Conference on Neural Information Processing Systems, NIPS 2005
CityVancouver, BC

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing


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