University of Cambridge > Talks.cam > Machine Learning Reading Group @ CUED > Submodularity for Machine Learning

Submodularity for Machine Learning

Add to your list(s) Download to your calendar using vCal

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: http://www.submodularity.org/

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

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2019 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity