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Poster
Learning via Gaussian Herding
Yacov Crammer · Daniel Lee

Wed Dec 08 12:00 AM -- 12:00 AM (PST) @
in Poster Session »

We introduce a new family of online learning algorithms based upon constraining the velocity flow over a distribution of weight vectors. In particular, we show how to effectively herd a Gaussian weight vector distribution by trading off velocity constraints with a loss function. By uniformly bounding this loss function, we demonstrate how to solve the resulting optimization analytically. We compare the resulting algorithms on a variety of real world datasets, and demonstrate how these algorithms achieve state-of-the-art robust performance, especially with high label noise in the training data.

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Author Information

Yacov Crammer (Technion)
Daniel Lee (Samsung Research/Cornell University)

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