University of Cambridge > Talks.cam > Machine Learning @ CUED > Efficient Sampling with Kernel Herding

  Browse A-Z

Log in

University Account External (via Google)

🔐 Log In

Information on

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

ℹ️ Information

Efficient Sampling with Kernel Herding

Download to your calendar using vCal

If you have a question about this talk, please contact Zoubin Ghahramani .

The herding algorithm was proposed as a deterministic dynamic system that integrates learning and inference for discrete Markov Random Fields. In this talk, we take a dual view of herding and extend it to continuous spaces by using the kernel trick. The resulting “kernel herding” is an infinite memory deterministic algorithm that approximates a PDF with a collection of samples. We show that kernel herding decreases the error of expectations of functions in the Hilbert space much faster than the usual iid random samples. If time permits, I’ll also talk about the recent development and applications of the herding algorithm.

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

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Hybrid - Maxwell Centre & on Zoom Average on weekdays: 8940.87356322 kWh Centre for Mathematical Sciences MR2, CMS

Other talks

Differences between Robin and Neumann eigenvalues Title to be confirmed Beyond Hubble: Studying the Earliest Galaxies with the James Webb Space Telescope Title to follow Dinner CSAR lecture: Decarbonizing road freight (TBC)
 

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