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Function estimation on large graphs with missing data

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SNAW05 - Bayesian methods for networks

Co-author: Harry van Zanten (University of Amsterdam)

There are various problems in statistics and machine learning that involve making an inference about a function on a graph. I will present a Bayesian approach to estimating a smooth function in the context of regression and classification problems on graphs. I will discuss the mathematical framework that allows to study the performance of nonparametric function estimation methods on large graphs. I will review theoretical results that show how to achieve asymptotically optimal Bayesian regularization under geometry conditions on the families of the graphs and the smoothness assumption on the true function. Both assumptions are formulated in terms of graph Laplacian. I will also discuss the case of “uniformly distributed” missing observations and investigate the generalization performance for various missing mechanisms.

This talk is part of the Isaac Newton Institute Seminar Series series.

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