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Informational Geometry

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If you have a question about this talk, please contact Rowan McAllister.

Abstract: We will review the max-ent interpretation of exponential families, and it’s connection to information projections. After exploring some simple results about I-projections, Fisher information, and their relation to ideas in machine learning, we will explain how to geometrically interpret AdaBoost in this framework.

Optional Background: Familiarity with Lagrange Multipliers/Lagrange duality will be helpful

This talk is part of the Machine Learning Reading Group @ CUED series.

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