arXiv Machine Learning By Benjamin Grimmer, Qining Shen

Exact Theory and Optimal Stepsizes for Stochastic Gradient Descent in Smooth Convex Minimization

Read the original on arXiv Machine Learning →

arXiv:2610. 42048v1 Announce Type: cross Abstract: This work considers the design of stepsize schedules for stochastic gradient descent in smooth convex optimization.

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arXiv Machine Learning
Jul 2

Towards Weaker Variance Assumptions for Stochastic Optimization

arXiv:2504. 09951v2 Announce Type: replace-cross Abstract: We revisit a classical assumption for analyzing stochastic gradient algorithms where the squared norm of the stochastic subgradient (or the variance for smooth problems) is allowed to grow as fast as the squared norm of the optimization variable.

By Ahmet Alacaoglu, Yura Malitsky, Stephen J. Wright