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arXiv Machine Learning By Liviu Aolaritei, Lucas L\'evy, Francis Bach, Michael I. Jordan

Beyond Optimal Rates in Stochastic Optimization: Trajectory-Adaptive Stopping Rules

Read the original on arXiv Machine Learning →

arXiv:2608. 25551v1 Announce Type: new Abstract: Stochastic gradient descent (SGD) is typically analyzed at a deterministic horizon chosen before the algorithm is run, even though practical stopping decisions are made adaptively by inspecting the evolving trajectory.

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