PS-Clip-SGD achieves optimal in-expectation convergence rates for non-convex optimization under heavy-tailed gradient noise, with matching high-probability guarantees, and outperforms standard methods on AlexNet trained on CIFAR-100.
Proceedings of the 36th International Conference on Machine Learning , pages =
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Robust and Fast Training via Per-Sample Clipping
PS-Clip-SGD achieves optimal in-expectation convergence rates for non-convex optimization under heavy-tailed gradient noise, with matching high-probability guarantees, and outperforms standard methods on AlexNet trained on CIFAR-100.