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Title: Improved Convergence in High Probability of Clipped Gradient Methods with Heavy Tailed Noise
In this work, we study the convergence \emph{in high probability} of clipped gradient methods when the noise distribution has heavy tails, i.e., with bounded $p$th moments, for some $1 more » « less
Award ID(s):
1750716
PAR ID:
10493977
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Curran Associates, Inc.
Date Published:
Journal Name:
Advances in Neural Information Processing Systems
Volume:
36
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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