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Andrew Blumberg

2 accepted papers

2023

A Framework for Fast and Stable Representations of Multiparameter Persistent Homology Decompositions

NeurIPS 2023poster

Topological data analysis (TDA) is an area of data science that focuses on using invariants from algebraic topology to provide multiscale shape descriptors for geometric data sets such as point clouds. One of the most important such descriptors is persistent homology, which encodes the change in sha…

2020

Multiparameter Persistence Image for Topological Machine Learning

NeurIPS 2020poster

In the last decade, there has been increasing interest in topological data analysis, a new methodology for using geometric structures in data for inference and learning. A central theme in the area is the idea of persistence, which in its most basic form studies how measures of shape change as a sca…