University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Approximate kernel embeddings of distributions

Approximate kernel embeddings of distributions

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.

STS - Statistical scalability

Kernel embeddings of distributions and the Maximum Mean Discrepancy (MMD), the resulting probability metric, are useful tools for fully nonparametric hypothesis testing and for learning on distributional inputs; i.e., where labels are only observed at an aggregate level. I will give an overview of this framework and describe the use of large-scale approximations to kernel embeddings in the context of Bayesian approaches to learning on distributions and in the context of distributional covariate shift; e.g., where measurement noise on the training inputs differs from that on the testing inputs.



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.

 

© 2006-2020 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity