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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
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