AdaGrad achieves the first provable convergence rate under heavy-tailed noise (4/3 < p ≤ 2) in non-convex settings without knowing p, plus an algorithm-dependent lower bound and an improved rate for AdaGrad-Norm under a mild extra assumption.
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Can Adaptive Gradient Methods Converge under Heavy-Tailed Noise? A Case Study of AdaGrad
AdaGrad achieves the first provable convergence rate under heavy-tailed noise (4/3 < p ≤ 2) in non-convex settings without knowing p, plus an algorithm-dependent lower bound and an improved rate for AdaGrad-Norm under a mild extra assumption.