Clipped Gradient Methods for Nonsmooth Convex Optimization under Heavy-Tailed Noise: A Refined Analysis

The article presents a refined analysis of clipped gradient methods for nonsmooth convex optimization in the presence of heavy-tailed noise. This suggests a focus on theoretical advancements in optimization algorithms, particularly those dealing with noisy data and non-differentiable functions. The use of "refined analysis" implies an improvement or extension of existing understanding.

• •Focus on optimization algorithms.
• •Addresses heavy-tailed noise.
• •Deals with non-differentiable functions.
• •Presents a refined analysis, suggesting improvements over existing methods.