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Approximate kernel embeddings of distributions

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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.

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Unilever Lecture Theatre, Unilever Centre, Department of Chemistry Bateman Auditorium, Gonville and Caius Webinar & LT1, Computer Laboratory, William Gates Building.

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