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Gradient Clipping Helps in Non-Smooth Stochastic Optimization with Heavy-Tailed Noise
In our paper, we resolve this issue and derive the first high-probability convergence results with logarithmical dependence on the confidence level for non-smooth convex stochastic optimization problems with non-sub-Gaussian (heavy-tailed) noise.
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