Web: http://arxiv.org/abs/2206.07252

June 16, 2022, 1:10 a.m. | Courtney Paquette, Elliot Paquette, Ben Adlam, Jeffrey Pennington

cs.LG updates on arXiv.org arxiv.org

Stochastic gradient descent (SGD) is a pillar of modern machine learning,
serving as the go-to optimization algorithm for a diverse array of problems.
While the empirical success of SGD is often attributed to its computational
efficiency and favorable generalization behavior, neither effect is well
understood and disentangling them remains an open problem. Even in the simple
setting of convex quadratic problems, worst-case analyses give an asymptotic
convergence rate for SGD that is no better than full-batch gradient descent
(GD), and …

arxiv ml regularization risk

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