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Invited Talk - Anima Anandkumar: Stein’s method for understanding optimization in neural networks.
Anima Anandkumar

Sat Jun 15 09:45 AM -- 10:30 AM (PDT) @
in Stein’s Method for Machine Learning and Statistics »

Training neural networks is a challenging non-convex optimization problem. Stein’s method provides a novel way to change optimization problem to a tensor decomposition problem for guaranteed training of two-layer neural networks. We provide risk bounds for our proposed method, with a polynomial sample complexity in the relevant parameters, such as input dimension and number of neurons. Our training method is based on tensor decomposition, which provably converges to the global optimum, under a set of mild non-degeneracy conditions. This provides insights into role of generative process for tractability of supervised learning.

Author Information

Anima Anandkumar (Amazon AI & Caltech)

Anima Anandkumar is a Bren Professor at Caltech and Director of ML Research at NVIDIA. She was previously a Principal Scientist at Amazon Web Services. She is passionate about designing principled AI algorithms and applying them to interdisciplinary domains. She has received several honors such as the IEEE fellowship, Alfred. P. Sloan Fellowship, NSF Career Award, Young investigator awards from DoD, Venturebeat’s “women in AI” award, NYTimes GoodTech award, and Faculty Fellowships from Microsoft, Google, Facebook, and Adobe. She is part of the World Economic Forum's Expert Network. She has appeared in the PBS Frontline documentary on the “Amazon empire” and has given keynotes in many forums such as the TEDx, KDD, ICLR, and ACM. Anima received her BTech from Indian Institute of Technology Madras, her PhD from Cornell University, and did her postdoctoral research at MIT and assistant professorship at University of California Irvine.

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