New in-expectation convergence guarantees for SMD, ASMD (convex) and SGD, SGDM (nonconvex) under heavy-tailed noise without bounded-domain restrictions or algorithmic modifications.
Stochastic nonsmooth convex optimization with heavy-tailed noises: High-probability bound, in-expectation rate and initial distance adaptation.arXiv preprint arXiv:2303.12277, 2023
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Presents a self-normalized subsampling procedure for asymptotically valid confidence regions from SGD iterates under both finite and infinite variance assumptions.
DADA is a parameter-free dual averaging method for convex optimization that adapts to local function growth and applies to nonsmooth, smooth, Holder-smooth, and other classes for both constrained and unbounded domains without prior knowledge of iteration count or accuracy.
citing papers explorer
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In-Expectation Convergence of Stochastic Gradient Methods under Heavy-Tailed Noise
New in-expectation convergence guarantees for SMD, ASMD (convex) and SGD, SGDM (nonconvex) under heavy-tailed noise without bounded-domain restrictions or algorithmic modifications.
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Statistical Inference for Stochastic Gradient Descent Beyond Finite Variance
Presents a self-normalized subsampling procedure for asymptotically valid confidence regions from SGD iterates under both finite and infinite variance assumptions.
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DADA: Dual Averaging with Distance Adaptation
DADA is a parameter-free dual averaging method for convex optimization that adapts to local function growth and applies to nonsmooth, smooth, Holder-smooth, and other classes for both constrained and unbounded domains without prior knowledge of iteration count or accuracy.