Research Seminar: "Accelerated Gradient Methods on Riemannian Manifolds" by Prof. Suvrit Sra скачать в хорошем качестве

Research Seminar: "Accelerated Gradient Methods on Riemannian Manifolds" by Prof. Suvrit Sra

Fall 2020 SIP Seminar Series: October 21, 2020 [http://www.inspirelab.us/seminars/] Speaker: Prof. Suvrit Sra Title: Accelerated Gradient Methods on Riemannian Manifolds Abstract: This talks lies at the interface of geometry and optimization. In particular, I’ll talk about geodesically convex optimization problems, a vast class of non-convex optimization problems that admit tractable global optimization. I’ll provide some background on this class and some canonical examples as motivation. The bulk of the talk thereafter will be devoted to a recent, long-sought result of potentially fundamental value: an accelerated gradient method for Riemannian manifolds. Toward developing this method, we will revisit Nesterov’s (Euclidean) estimate sequence technique and develop a conceptually simple alternative. We build on this alternative to obtain new results in the Riemannian setting. We localize the key difficulty into “metric distortion,” which we then control carefully to obtain the first (global) accelerated Riemannian-gradient method. Biography: Suvrit Sra is an Associate Professor of EECS at MIT, and a core member of the Laboratory for Information and Decision Systems (LIDS), the Institute for Data, Systems, and Society (IDSS), as well as a member of MIT-ML and Statistics groups. He obtained his PhD in Computer Science from the University of Texas at Austin. Before moving to MIT, he was a Senior Research Scientist at the Max Planck Institute for Intelligent Systems, Tübingen, Germany. His research bridges mathematical areas such as differential geometry, matrix analysis, convex analysis, probability theory, and optimization with machine learning. He founded the OPT (Optimization for Machine Learning) series of workshops, held from OPT2008–2017 at the NeurIPS (erstwhile NIPS) conference. He has co-edited a book with the same name (MIT Press, 2011). He is also a co-founder and chief scientist of macro-eyes, a global healthcare+AI startup.