Spotlight talks from paper submissions
in
Workshop: Beyond first order methods in machine learning systems
Spotlight talks
Damien Scieur · Konstantin Mishchenko · Rohan Anil
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Abstract
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2019 Spotlight talks from paper submissions
Abstract:
Symmetric Multisecant quasi-Newton methods. Damien Scieur (Samsung AI Research Montreal); Thomas Pumir (Princeton University); Nicolas Boumal (Princeton University)
Stochastic Newton Method and its Cubic Regularization via Majorization-Minimization. Konstantin Mishchenko (King Abdullah University of Science & Technology (KAUST)); Peter Richtarik (KAUST); Dmitry Koralev (KAUST)
Full Matrix Preconditioning Made Practical. Rohan Anil (Google); Vineet Gupta (Google); Tomer Koren (Google); Kevin Regan (Google); Yoram Singer (Princeton)
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