Tag: UCLA

Geometry of Big Data – Tuesday session
Session 1 – Graphbased persistence The talk On the density of expected persistence diagrams and its kernel based estimation is given by Frederic Chazal. A draft is available on arxiv. Grow circles around point data to generate a graph whenever other points meet the circle and produce a persistent homology of filtered simplicial complexes (e.g […]

Geometry of Big Data – Monday session
All talks are summarised in my words which may not accurately represent the authors’ opinion. The focus is on aspects I found interesting. Please refer to the authors’ work for more details. Session 1 – Learning DAGs The talk DAGs with NO TEARS: Continuous Optimization for Structure Learning is given by Pradeep Ravikumar. A draft […]

Starting “Geometry and Learning from Data in 3D and Beyond” at IPAM, UCLA
Today is the first day of my stay at the Institute for Pure and Applied Mathematics (IPAM) at University of California Los Angeles (UCLA). Over the coming weeks I wil try to discuss interesting talks here at the long course Geometry and Learning from Data in 3D and Beyond. Stay tuned for the first workshop […]