AI RESEARCH
Lower Bounds and Proximally Anchored SGD for Non-Convex Minimization Under Unbounded Variance
arXiv CS.LG
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ArXi:2604.16620v1 Announce Type: new Analysis of Stochastic Gradient Descent (SGD) and its variants typically relies on the assumption of uniformly bounded variance, a condition that frequently fails in practical non-convex settings, such as neural network