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

## Approximate kernel embeddings of distributionsAdd to your list(s) Download to your calendar using vCal - Dino Sejdinovic (University of Oxford)
- Tuesday 01 May 2018, 11:00-12:00
- Seminar Room 2, Newton Institute.
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. ## This talk is included in these lists:- All CMS events
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