AI RESEARCH
Learning Theory of the SVRG: Generalization and Convergence Analysis
arXiv CS.AI
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ArXi:2605.28513v1 Announce Type: cross Variance reduction (VR) methods employ stochastic gradients with decreasing variance, and they have been widely applied to solve large-scale optimization problems in machine learning because of their efficiency. Existing theoretical studies of VR methods are mainly focused on the convergence analysis, leaving the generalization behavior largely unexplored.