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I am interested in Machine Learning. I am currently focused on
- Gaussian Mixture Models where I am looking at clustering of Gaussian mixtures at optimum solutions
- Gaussian Processes with a focus on applying Gaussian process regression to discrete spaces in particular learning functions defined on the nodes of graphs.
I have been looking at predicting the learning curves of the GP (mean square error as number of examples is increased) and hope to look at model mismatch and evolving graphs in the future.
Exact learning curves for Gaussian process regression on large random graphs
as author at Video Journal of Machine Learning Abstracts - Volume 1,