John ellipsoid approximation in the leverage-score model achieves doubly logarithmic accuracy cost after setup by using last-iterate acceleration and Newton steps instead of averaging.
Gutman and Javier F
2 Pith papers cite this work. Polarity classification is still indexing.
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math.OC 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
New in-expectation convergence guarantees for SMD, ASMD (convex) and SGD, SGDM (nonconvex) under heavy-tailed noise without bounded-domain restrictions or algorithmic modifications.
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Beyond Averaging in John Ellipsoid Approximation: High-Accuracy Algorithms in the Leverage-Score Model
John ellipsoid approximation in the leverage-score model achieves doubly logarithmic accuracy cost after setup by using last-iterate acceleration and Newton steps instead of averaging.
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In-Expectation Convergence of Stochastic Gradient Methods under Heavy-Tailed Noise
New in-expectation convergence guarantees for SMD, ASMD (convex) and SGD, SGDM (nonconvex) under heavy-tailed noise without bounded-domain restrictions or algorithmic modifications.