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Statistics > Machine Learning

arXiv:2107.10884 (stat)
[Submitted on 22 Jul 2021 (v1), last revised 19 Feb 2022 (this version, v3)]

Title:Structured second-order methods via natural gradient descent

Authors:Wu Lin, Frank Nielsen, Mohammad Emtiyaz Khan, Mark Schmidt
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Abstract:In this paper, we propose new structured second-order methods and structured adaptive-gradient methods obtained by performing natural-gradient descent on structured parameter spaces. Natural-gradient descent is an attractive approach to design new algorithms in many settings such as gradient-free, adaptive-gradient, and second-order methods. Our structured methods not only enjoy a structural invariance but also admit a simple expression. Finally, we test the efficiency of our proposed methods on both deterministic non-convex problems and deep learning problems.
Comments: Fixed some typos and added a new figure. ICML 2021 workshop paper. A short version of arXiv:2102.07405 with a focus on optimization tasks
Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG)
Cite as: arXiv:2107.10884 [stat.ML]
  (or arXiv:2107.10884v3 [stat.ML] for this version)
  https://doi.org/10.48550/arXiv.2107.10884

Submission history

From: Wu Lin [view email]
[v1] Thu, 22 Jul 2021 19:03:53 UTC (715 KB)
[v2] Fri, 15 Oct 2021 18:34:42 UTC (715 KB)
[v3] Sat, 19 Feb 2022 20:50:49 UTC (744 KB)
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