University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Statistical Relational Learning: Review and Recent Advances

Statistical Relational Learning: Review and Recent Advances

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

If you have a question about this talk, please contact INI IT.

SNAW05 - Bayesian methods for networks

Statistical relational learning (SRL) is a subfield of machine learning that combines relational representations (from databases and AI) with probabilistic modeling techniques (most often graphical models)for modeling network data (typically richly structured multi-relational and multi-model networks).  In this talk, I will briefly review some SRL modeling techniques, and then I will introduce hinge-loss Markov random fields (HL-MRFs), a new kind of probabilistic graphical model that supports scalable collective inference from richly structured data.  HL-MRFs unify three different approaches to convex inference: LP approximations for randomized algorithms, local relaxations for probabilistic graphical models, and inference in soft logic.  I will show that all three lead to the same inference objective.  This makes inference in HL-MRFs highly scalable.   Along the way, I will describe several successful applications of HL-MRFs and I will describe probabilistic soft logic, a declarative language for defining HL-MRFS.

This talk is part of the Isaac Newton Institute Seminar Series series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2023 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity