Author

Jun Liu

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#machine learning Preprint Sep 2026

Almost Sure Convergence Analysis of Stochastic Gradient Methods with Clipping and Additive Noise

It is proved that SGD with clipping and additive Gaussian noise (SGD-CN) converges almost surely (a.s.) under smoothness and uniformly bounded stochastic-gradient noise assumptions, provided the step sizes satisfy some standard decaying conditions.

Amartya Mukherjee, Jun Liu · 0 citations