Dino Sejdinovic - Developments at the Interface Between Kernel Embeddings and Gaussian Processes
Channel: Secondmind
Date: 2022-01-20

Abstract:
Reproducing kernel Hilbert spaces (RKHS) provide a powerful framework, termed kernel mean embeddings, for representing probability distributions, enabling nonparametric statistical inference in a variety of applications. I will give an overview of this framework and present some of its recent developments which combine RKHS formalism with Gaussian process modelling. Some recent applications include causal data fusion, where data of different quality needs to be combined in order to estimate the average treatment effect, as well as statistical downscaling using potentially unmatched multi-resolution data.

References:
1. S. L. Chau, S. Bouabid, and D. Sejdinovic, Deconditional Downscaling with Gaussian Processes, in Advances in Neural Information Processing Systems (NeurIPS), 2021, forthcoming. https://arxiv.org/pdf/2105.12909.pdf
2. S. L. Chau, J.-F. Ton, J. Gonzalez, Y. W. Teh, and D. Sejdinovic, BayesIMP: Uncertainty Quantification for Causal Data Fusion, in Advances in Neural Information Processing Systems (NeurIPS), 2021, forthcoming. https://arxiv.org/pdf/2106.03477.pdf

Speaker: 
Dr Dino Sejdinovic is anAssociate Professor at the Department of Statistics, University of Oxford, a Fellow of Mansfield College, Oxford, and a Turing Fellow of the Alan Turing Institute. Personal website can be found at http://www.stats.ox.ac.uk/~sejdinov.

This talk was given at Secondmind Labs, as a part of our (virtual) research seminar. Our research seminar is where we exchange ideas with guest speakers, keeping you up to date with the latest developments and inspiring research topics. Occasionally, Secondmind researchers present their own work as well. You can find a complete list of speakers at https://www.secondmind.ai/labs/seminars/. Learn more about Secondmind Labs at https://www.secondmind.ai/labs/​