arXiv Machine Learning By Haoyuan Cai, Sulaiman A. Alghunaim, Ali H. Sayed

Accelerated Stochastic Min-Max Optimization Based on Bias-corrected Momentum

Read the original on arXiv Machine Learning →

arXiv:2406. 13041v3 Announce Type: replace Abstract: Lower-bound analyses for nonconvex strongly-concave minimax optimization problems have shown that stochastic first-order algorithms require at least $\mathcal{O}(\varepsilon^{-4})$ sample complexity to find an $\varepsilon$-stationary point.

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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