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Why Clipping Matters in AdaGrad? Toward a High-Probability Theory under Generalized Smoothness
We analyze the original same-step coordinate-wise AdaGrad under generalized smoothness and heavy-tailed noise with bounded variance. In this setting, local curvature may grow sub-quadratically with the gradient norm, and stochastic gradients are assumed to have only bounded conditional second moment
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