Almost Sure Convergence Analysis of Stochastic Gradient Methods with Clipping and Additive Noise
Stochastic gradient descent (SGD) with gradient clipping and additive noise has become a standard technique for training machine learning models, particularly in applications requiring robustness or privacy guarantees. However, clipping introduces a bias in stochastic gradients, while additive noise introduces additional variance, making the long-run behaviour of individual optimization trajectories difficult to characterize. In this work, we prove 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. Our anal...
| Authors | Amartya Mukherjee, Jun Liu |
|---|---|
| Primary category | cs.LG |
| Published | 10 September 2026 |
| Last updated at source | 16 September 2026 |
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