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

  Browse A-Z

Log in

Googledetails Microsoftdetails

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹ️ Information

Approximate Inference in Gaussian Process Models

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. Quiñonero-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.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Lecture Theatre 2, Department of Veterinary Medicine, Cambridge Hybrid: Seminar room, SPRI & Zoom - email organiser for details Seminar time is 1pm BST in Room FW 11, Willam Gates Hall. Zoom link: https://cl-cam-ac-uk.zoom.us/j/4361570789?pwd=Nkl2T3ZLaTZwRm05bzRTOUUxY3Q4QT09&from=addon

Other talks

Group Presentation 4 Climate, uncertainty, and the slippery measurement of Antarctic ice Talk title TBC Grand Rounds - Equine TBC Using numerical simulations for solving spectral geometry problems
 

© 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity