Matrix Completion has No Spurious Local Minimum
暂无分享,去创建一个
[1] Kurt Hornik,et al. Neural networks and principal component analysis: Learning from examples without local minima , 1989, Neural Networks.
[2] R. Pemantle,et al. Nonconvergence to Unstable Points in Urn Models and Stochastic Approximations , 1990 .
[3] Renato D. C. Monteiro,et al. A nonlinear programming algorithm for solving semidefinite programs via low-rank factorization , 2003, Math. Program..
[4] Tommi S. Jaakkola,et al. Weighted Low-Rank Approximations , 2003, ICML.
[5] Tommi S. Jaakkola,et al. Maximum-Margin Matrix Factorization , 2004, NIPS.
[6] Nathan Srebro,et al. Fast maximum margin matrix factorization for collaborative prediction , 2005, ICML.
[7] Adi Shraibman,et al. Rank, Trace-Norm and Max-Norm , 2005, COLT.
[8] Yurii Nesterov,et al. Cubic regularization of Newton method and its global performance , 2006, Math. Program..
[9] Shimon Ullman,et al. Uncovering shared structures in multiclass classification , 2007, ICML '07.
[10] Andrea Montanari,et al. Matrix Completion from Noisy Entries , 2009, J. Mach. Learn. Res..
[11] Yehuda Koren,et al. The BellKor Solution to the Netflix Grand Prize , 2009 .
[12] Andrea Montanari,et al. Matrix completion from a few entries , 2009, 2009 IEEE International Symposium on Information Theory.
[13] R. Oliveira. Sums of random Hermitian matrices and an inequality by Rudelson , 2010, 1004.3821.
[14] Robert Tibshirani,et al. Spectral Regularization Algorithms for Learning Large Incomplete Matrices , 2010, J. Mach. Learn. Res..
[15] Emmanuel J. Candès,et al. The Power of Convex Relaxation: Near-Optimal Matrix Completion , 2009, IEEE Transactions on Information Theory.
[16] Sham M. Kakade,et al. A tail inequality for quadratic forms of subgaussian random vectors , 2011, ArXiv.
[17] Yi Ma,et al. Robust principal component analysis? , 2009, JACM.
[18] Benjamin Recht,et al. A Simpler Approach to Matrix Completion , 2009, J. Mach. Learn. Res..
[19] Ewout van den Berg,et al. 1-Bit Matrix Completion , 2012, ArXiv.
[20] Emmanuel J. Candès,et al. Exact Matrix Completion via Convex Optimization , 2008, Found. Comput. Math..
[21] Martin J. Wainwright,et al. Restricted strong convexity and weighted matrix completion: Optimal bounds with noise , 2010, J. Mach. Learn. Res..
[22] Po-Ling Loh,et al. Regularized M-estimators with nonconvexity: statistical and algorithmic theory for local optima , 2013, J. Mach. Learn. Res..
[23] Prateek Jain,et al. Low-rank matrix completion using alternating minimization , 2012, STOC '13.
[24] Mary Wootters,et al. Fast matrix completion without the condition number , 2014, COLT.
[25] Po-Ling Loh,et al. Support recovery without incoherence: A case for nonconvex regularization , 2014, ArXiv.
[26] Moritz Hardt,et al. Understanding Alternating Minimization for Matrix Completion , 2013, 2014 IEEE 55th Annual Symposium on Foundations of Computer Science.
[27] Trevor J. Hastie,et al. Matrix completion and low-rank SVD via fast alternating least squares , 2014, J. Mach. Learn. Res..
[28] Prateek Jain,et al. Fast Exact Matrix Completion with Finite Samples , 2014, COLT.
[29] Zhi-Quan Luo,et al. Guaranteed Matrix Completion via Non-Convex Factorization , 2014, IEEE Transactions on Information Theory.
[30] Furong Huang,et al. Escaping From Saddle Points - Online Stochastic Gradient for Tensor Decomposition , 2015, COLT.
[31] Zhaoran Wang,et al. A Nonconvex Optimization Framework for Low Rank Matrix Estimation , 2015, NIPS.
[32] Christopher De Sa,et al. Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems , 2014, ICML.
[33] John Wright,et al. When Are Nonconvex Problems Not Scary? , 2015, ArXiv.
[34] Martin J. Wainwright,et al. Fast low-rank estimation by projected gradient descent: General statistical and algorithmic guarantees , 2015, ArXiv.
[35] John D. Lafferty,et al. Convergence Analysis for Rectangular Matrix Completion Using Burer-Monteiro Factorization and Gradient Descent , 2016, ArXiv.
[36] Nathan Srebro,et al. Global Optimality of Local Search for Low Rank Matrix Recovery , 2016, NIPS.
[37] Nicolas Boumal,et al. On the low-rank approach for semidefinite programs arising in synchronization and community detection , 2016, COLT.
[38] Yuanzhi Li,et al. Recovery guarantee of weighted low-rank approximation via alternating minimization , 2016, ICML.
[39] Michael I. Jordan,et al. Gradient Descent Converges to Minimizers , 2016, ArXiv.
[40] Max Simchowitz,et al. Low-rank Solutions of Linear Matrix Equations via Procrustes Flow , 2015, ICML.