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The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning
Jesse Krijthe · Marco Loog

Thu Dec 06 02:00 PM -- 04:00 PM (PST) @ Room 517 AB #111

Consider a classification problem where we have both labeled and unlabeled data available. We show that for linear classifiers defined by convex margin-based surrogate losses that are decreasing, it is impossible to construct \emph{any} semi-supervised approach that is able to guarantee an improvement over the supervised classifier measured by this surrogate loss on the labeled and unlabeled data. For convex margin-based loss functions that also increase, we demonstrate safe improvements \emph{are} possible.

Author Information

Jesse Krijthe (Radboud University Nijmegen)
Marco Loog (Delft University of Technology)

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