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Title: Geometric approaches to inference: Non-Euclidean data and networks Authors:  Dena Asta - The Ohio State University (United States) [presenting]
Abstract: The purpose is to describe applications of geometry to large-scale data analysis. An overriding theme is that an understanding of the relevant geometric structure in the data is useful for efficient and large-scale statistical analyses. Firstly, we will discuss geometric methods for non-parametric methods in non-Euclidean spaces. Secondly, we will discuss a geometric approach to network inference by focusing on the Riemannian geometry of CLS models.