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Submodularity for Machine Learning

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If you have a question about this talk, please contact Shakir Mohamed.

Recently, a fundamental problem structure has emerged as very useful in a variety of machine learning applications: Submodularity is an intuitive diminishing returns property, stating that adding an element to a smaller set helps more than adding it to a larger set. Similarly to convexity, submodularity allows one to efficiently find provably (near-) optimal solutions for large problems.

We will give an introduction to this topic covering:

Introduction: Why should you care about submodularity?

Basic theory of Submodular set functions: Definitions, Operations preserving submodularity, relationship to convexity

Optimisation: Example problems involving minimisation and maximisation of submodular functions.

The RCC will be based on the tutorial found here:

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

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