University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Large Graph Limits of Learning Algorithms

Large Graph Limits of Learning Algorithms

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

If you have a question about this talk, please contact info@newton.ac.uk.

UNQW04 - UQ for inverse problems in complex systems

Many problems in machine learning require the classification of high dimensional data. One methodology to approach such problems is to construct a graph whose vertices are identified with data points, with edges weighted according to some measure of affinity between the data points. Algorithms such as spectral clustering, probit classification and the Bayesian level set method can all be applied in this setting. The goal of the talk is to describe these algorithms for classification, and analyze them in the limit of large data sets. Doing so leads to interesting problems in the calculus of variations, Bayesian inverse problems and in Monte Carlo Markov Chain, all of which will be highlighted in the talk. These limiting problems give insight into the structure of the classification problem, and algorithms for it.    

Collaboration with:  
Andrea Bertozzi (UCLA)
Michael Luo (UCLA)
Kostas Zygalakis (Edinburgh)
https://arxiv.org/abs/1703.08816  
and  
Matt Dunlop (Caltech)
Dejan Slepcev (CMU)
Matt Thorpe (Cambridge)
(forthcoming paper)

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.

 

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