SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Bamdev Mishra and Rodolphe Sepulchre. Riemannian preconditioning. SIAM Journal on Optimization, 26(1):635-660, 2016.
Yousef Saad. Iterative methods for sparse linear systems. SIAM, 2003.
Dominik Stöger and Mahdi Soltanolkotabi. Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction. Advances in Neural Information Processing Systems, 34:23831-23843, 2021.
Ruoyu Sun and Zhi-Quan Luo. Guaranteed matrix completion via non-convex factorization. IEEE Transactions on Information Theory, 62(11):6535-6579, 2016.
Jared Tanner and Ke Wei. Low rank matrix completion by alternating steepest descent methods. Applied and Computational Harmonic Analysis, 40(2):417-429, 2016.
Terence Tao. Topics in random matrix theory, volume 132. American Mathematical Soc., 2012.
Tian Tong. Scaled gradient methods for ill-conditioned low-rank matrix and tensor estimation. PhD thesis, Carnegie Mellon University, 2022.
Tian Tong, Cong Ma, and Yuejie Chi. Accelerating ill-conditioned low-rank matrix estimation via scaled gradient descent. The Journal of Machine Learning Research, 22(1):6639-6701, 2021.
Zaiwen Wen, Wotao Yin, and Yin Zhang. Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm. Mathematical Programming Computation, 4(4):333-361, 2012.
Xingyu Xu, Yandi Shen, Yuejie Chi, and Cong Ma. The power of preconditioning in overparameterized low-rank matrix sensing. arXiv preprint arXiv:2302.01186, 2023.
Tian Ye and Simon S Du. Global convergence of gradient descent for asymmetric low-rank matrix factorization. Advances in Neural Information Processing Systems, 34:1429-1439, 2021.
Jialun Zhang, Salar Fattahi, and Richard Y Zhang. Preconditioned gradient descent for overparameterized nonconvex matrix factorization. Advances in Neural Information Processing Systems, 34:5985-5996, 2021.
Tuo Zhao, Zhaoran Wang, and Han Liu. A nonconvex optimization framework for low rank matrix estimation. Advances in Neural Information Processing Systems, 2015:559-567, 2015.
Pini Zilber and Boaz Nadler. Inductive matrix completion: No bad local minima and a fast algorithm. In International Conference on Machine Learning, pages 27671-27692. PMLR, 2022.