Poster
Learning via Gaussian Herding
Yacov Crammer · Daniel Lee

Wed Dec 8th 12:00 -- 12:00 AM @ None #None

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.

Author Information

Yacov Crammer (Technion)
Daniel Lee (Cornell Tech)

More from the Same Authors