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Approximate kernel embeddings of distributions

Presented by: 
Dino Sejdinovic University of Oxford
Tuesday 1st May 2018 - 11:00 to 12:00
INI Seminar Room 2
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.

University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons