GT-DSGD achieves order-optimal high-probability rates O(log(1/δ)/sqrt(nT)) for non-convex and O(log(1/δ)/(nT)) for PL costs, matching the conditions used for mean-squared error bounds.
Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under (L 0, L1)-Smoothness
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High-Probability Convergence in Decentralized Stochastic Optimization with Gradient Tracking
GT-DSGD achieves order-optimal high-probability rates O(log(1/δ)/sqrt(nT)) for non-convex and O(log(1/δ)/(nT)) for PL costs, matching the conditions used for mean-squared error bounds.