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High-Probability Guarantees for SGD under $β$-Heavy-Tailed Gradient Noise
Stochastic gradient descent (SGD) is widely used to train machine learning models, but subsampling the training data introduces noise into its updates. The strength and applicability of high-probability guarantees therefore depend critically on how the tails of gradient noise are modeled. Reports of
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