Tighter bounds for structured estimation

Chuong B. Do*, Quoc Le, Choon Hui Teo, Olivier Chapelle, Alex Smola

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    39 Citations (Scopus)

    Abstract

    Large-margin structured estimation methods minimize a convex upper bound of loss functions. While they allow for efficient optimization algorithms, these convex formulations are not tight and sacrifice the ability to accurately model the true loss. We present tighter non-convex bounds based on generalizing the notion of a ramp loss from binary classification to structured estimation. We show that a small modification of existing optimization algorithms suffices to solve this modified problem. On structured prediction tasks such as protein sequence alignment and web page ranking, our algorithm leads to improved accuracy.

    Original languageEnglish
    Title of host publicationAdvances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference
    PublisherNeural Information Processing Systems
    Pages281-288
    Number of pages8
    ISBN (Print)9781605609492
    Publication statusPublished - 2009
    Event22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 - Vancouver, BC, Canada
    Duration: 8 Dec 200811 Dec 2008

    Publication series

    NameAdvances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

    Conference

    Conference22nd Annual Conference on Neural Information Processing Systems, NIPS 2008
    Country/TerritoryCanada
    CityVancouver, BC
    Period8/12/0811/12/08

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