Apr 21
Lower Bounds and Proximally Anchored SGD for Non-Convex Minimization Under Unbounded Variance
★★★★★
significance 2/5
The paper analyzes the complexity of non-convex optimization under the Blum-Gladyshev condition, which allows for unbounded variance. It establishes new information-theoretic lower bounds and introduces the Proximally Anchored Stochastic Approximation (PASTA) framework to mitigate variance explosion.
Why it matters
Establishing theoretical limits for optimization under unbounded variance addresses fundamental stability concerns in training large-scale non-convex models.
Tags
#optimization #sgd #non-convex #stochastic gradient descent #complexityRelated coverage
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