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We study the asymmetric matrix factorization problem under a natural nonconvex formulation with arbitrary overparametrization.
Early stopping-but when?
Lutz Prechelt · 1998
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Gradient descent for deep matrix factorization: Dynamics and implicit bias towards low rank
Hung-Hsu Chou, Carsten Gieshoff, Johannes Maly, and Holger Rauhut · 2020
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Rank overspecified robust matrix recovery: Subgradient method and exact recovery
Lijun Ding, Liwei Jiang, Yudong Chen, Qing Qu, and Zhihui Zhu · 2021
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Beyond Procrustes: Balancing-free gradient descent for asymmetric low-rank matrix sensing
Cong Ma, Yuanxin Li, and Yuejie Chi · 2021
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Dominik Stöger and Mahdi Soltanolkotabi · 2021
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Global convergence of gradient descent for asymmetric low-rank matrix factorization
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Preconditioned gradient descent for over-parameterized nonconvex matrix factorization
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Sharp global guarantees for nonconvex low-rank matrix recovery in the overparameterized regime
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On the computational and statistical complexity of over-parameterized matrix sensing
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