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Algorithmic Regularization in Model-free Overparametrized Asymmetric Matrix Factorization
[article]
2022
arXiv
pre-print
We study the asymmetric matrix factorization problem under a natural nonconvex formulation with arbitrary overparametrization. The model-free setting is considered, with minimal assumption on the rank or singular values of the observed matrix, where the global optima provably overfit. We show that vanilla gradient descent with small random initialization sequentially recovers the principal components of the observed matrix. Consequently, when equipped with proper early stopping, gradient
arXiv:2203.02839v2
fatcat:d2xua7c5ejecxoshfdgwguxywy