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arXiv Machine Learning By Dimitris Oikonomou, Nicolas Loizou

Safeguarded Stochastic Polyak Step Sizes for Non-smooth Optimization: Robust Performance Without Small (Sub)Gradients

Read the original on arXiv Machine Learning →

arXiv:2512. 02342v3 Announce Type: replace-cross Abstract: The stochastic Polyak step size (SPS) has proven to be a promising choice for stochastic gradient descent (SGD), delivering competitive performance relative to state-of-the-art methods on smooth convex and non-convex optimization problems, including deep neural network training.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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