University of Cambridge > Talks.cam > Machine Learning Reading Group @ CUED >  Approximate Inference in Gaussian Process Models

Approximate Inference in Gaussian Process Models

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

If you have a question about this talk, please contact David Duvenaud.

This week’s RCC will be an in-depth tutorial on approximate inference for Gaussian process models. We will be covering both sparse approximations, as well as approximate inference for non-Gaussian likelihoods. The tutorial will be given by Andrew McHutchon, and myself, David Duvenaud.

The papers we will cover are:

A unifying view of sparse approximate Gaussian process regression. J. QuiƱonero-Candela and C. E. Rasmussen. http://jmlr.csail.mit.edu/papers/volume6/quinonero-candela05a/quinonero-candela05a.pdf

and

Approximations for binary Gaussian process classification. H. Nickisch and C. E. Rasmussen. http://jmlr.csail.mit.edu/papers/volume9/nickisch08a/nickisch08a.pdf (Sections 1-3 would be the most fruitful to read ahead of time)

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.

 

© 2006-2024 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity