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Several key questions remain unanswered regarding overparameterized learning models.
Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization
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Tight oracle inequalities for low-rank matrix recovery from a minimal number of noisy random measurements
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User-friendly tail bounds for sums of random matrices
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Global optimality of local search for low rank matrix recovery
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Provable efficient online matrix completion via non-convex stochastic gradient descent
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Low-rank solutions of linear matrix equations via procrustes flow
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Understanding deep learning requires rethinking generalization. corr abs/1611.03530 (2016)
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Gintare Karolina Dziugaite and Daniel M Roy · 2017
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Roman Vershynin · 2018
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Yuejie Chi, Yue M Lu, and Yuxin Chen · 2019
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The instructor’s guide to real induction
Pete L Clark · 2019
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Nonconvex rectangular matrix completion via gradient descent without ℓ 2 , ∞ \ell_{2,\infty} regularization
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Global convergence of gradient descent for asymmetric low-rank matrix factorization
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The global optimization geometry of low-rank matrix optimization
Zhihui Zhu, Qiuwei Li, Gongguo Tang, and Michael B Wakin · 2021
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Transformers learn through gradual rank increase
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Implicit balancing and regularization: Generalization and convergence guarantees for overparameterized asymmetric matrix sensing
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