{"list":"other","total":58,"rows":[{"id":"1607.01668","title":"Tensor Decomposition for Signal Processing and Machine Learning","created":"2016-07-06","cited_by_count":224},{"id":"1809.09573","title":"Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview","created":"2018-09-25","cited_by_count":160},{"id":"1407.1065","title":"Phase Retrieval via Wirtinger Flow: Theory and Algorithms","created":"2014-07-03","cited_by_count":125},{"id":"2012.08496","title":"Spectral Methods for Data Science: A Statistical Perspective","created":"2020-12-15","cited_by_count":112},{"id":"1411.8003","title":"Guaranteed Matrix Completion via Non-convex Factorization","created":"2014-11-28","cited_by_count":82},{"id":"1803.07726","title":"Gradient Descent with Random Initialization: Fast Global Convergence for Nonconvex Phase Retrieval","created":"2018-03-21","cited_by_count":71},{"id":"2005.08898","title":"Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent","created":"2020-05-18","cited_by_count":71},{"id":"1502.04689","title":"Exact tensor completion using t-SVD","created":"2015-02-16","cited_by_count":70},{"id":"1509.03025","title":"Fast low-rank estimation by projected gradient descent: General statistical and algorithmic guarantees","created":"2015-09-10","cited_by_count":70},{"id":"1711.10467","title":"Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution","created":"2017-11-28","cited_by_count":63},{"id":"1407.1785","title":"Novel methods for multilinear data completion and de-noising based on tensor-SVD","created":"2014-07-07","cited_by_count":56},{"id":"1703.02724","title":"Tensor SVD: Statistical and Computational Limits","created":"2017-03-08","cited_by_count":53},{"id":"1505.05114","title":"Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems","created":"2015-05-19","cited_by_count":50},{"id":"2002.11255","title":"An Optimal Statistical and Computational Framework for Generalized Tensor Estimation","created":"2020-02-26","cited_by_count":39},{"id":"1609.03240","title":"Non-square matrix sensing without spurious local minima via the Burer-Monteiro approach","created":"2016-09-12","cited_by_count":38},{"id":"1307.5870","title":"Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery","created":"2013-07-22","cited_by_count":37},{"id":"1705.07576","title":"Global Guarantees for Enforcing Deep Generative Priors by Empirical Risk","created":"2017-05-22","cited_by_count":37},{"id":"1911.04436","title":"Nonconvex Low-Rank Tensor Completion from Noisy Data","created":"2019-11-11","cited_by_count":35},{"id":"2008.11245","title":"Deep Networks and the Multiple Manifold Problem","created":"2020-08-25","cited_by_count":35},{"id":"1311.6182","title":"Robust Low-rank Tensor Recovery: Models and Algorithms","created":"2013-11-24","cited_by_count":34},{"id":"1405.1773","title":"On Tensor Completion via Nuclear Norm Minimization","created":"2014-05-07","cited_by_count":32},{"id":"1406.2784","title":"Provable Tensor Factorization with Missing Data","created":"2014-06-11","cited_by_count":31},{"id":"1904.10020","title":"Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence","created":"2019-04-22","cited_by_count":31},{"id":"1605.08285","title":"Solving Systems of Random Quadratic Equations via Truncated Amplitude Flow","created":"2016-05-26","cited_by_count":29},{"id":"2007.06753","title":"From Symmetry to Geometry: Tractable Nonconvex Problems","created":"2020-07-14","cited_by_count":29},{"id":"2010.13364","title":"Low-Rank Matrix Recovery with Scaled Subgradient Methods: Fast and Robust Convergence Without the Condition Number","created":"2020-10-26","cited_by_count":28},{"id":"1612.07866","title":"Spectral algorithms for tensor completion","created":"2016-12-23","cited_by_count":27},{"id":"1702.06980","title":"On Polynomial Time Methods for Exact Low Rank Tensor Completion","created":"2017-02-22","cited_by_count":27},{"id":"1802.08397","title":"Harnessing Structures in Big Data via Guaranteed Low-Rank Matrix Estimation","created":"2018-02-23","cited_by_count":25},{"id":"1511.04777","title":"Complete Dictionary Recovery over the Sphere II: Recovery by Riemannian Trust-region Method","created":"2015-11-15","cited_by_count":24},{"id":"1611.10349","title":"Non-Convex Projected Gradient Descent for Generalized Low-Rank Tensor Regression","created":"2016-11-30","cited_by_count":23},{"id":"1711.04934","title":"Statistically Optimal and Computationally Efficient Low Rank Tensor Completion from Noisy Entries","created":"2017-11-14","cited_by_count":23},{"id":"1802.06463","title":"Guaranteed Recovery of One-Hidden-Layer Neural Networks via Cross Entropy","created":"2018-02-18","cited_by_count":22},{"id":"1706.05598","title":"On the Optimization Landscape of Tensor Decompositions","created":"2017-06-18","cited_by_count":21},{"id":"1304.4672","title":"Low-Rank Matrix and Tensor Completion via Adaptive Sampling","created":"2013-04-17","cited_by_count":19},{"id":"1504.06785","title":"Complete Dictionary Recovery over the Sphere","created":"2015-04-26","cited_by_count":19},{"id":"1605.08257","title":"Low-rank tensor completion: a Riemannian manifold preconditioning approach","created":"2016-05-26","cited_by_count":19},{"id":"1610.01690","title":"Low-tubal-rank Tensor Completion using Alternating Minimization","created":"2016-10-05","cited_by_count":18},{"id":"1810.10702","title":"Subgradient Descent Learns Orthogonal Dictionaries","created":"2018-10-25","cited_by_count":17},{"id":"1806.02511","title":"Exact Low Tubal Rank Tensor Recovery from Gaussian Measurements","created":"2018-06-07","cited_by_count":16},{"id":"2006.03134","title":"Tensor Completion Made Practical","created":"2020-06-04","cited_by_count":15},{"id":"1702.06237","title":"Exact tensor completion with sum-of-squares","created":"2017-02-21","cited_by_count":14},{"id":"1911.03804","title":"ISLET: Fast and Optimal Low-rank Tensor Regression via Importance Sketching","created":"2019-11-09","cited_by_count":14},{"id":"1802.06286","title":"Nonconvex Matrix Factorization from Rank-One Measurements","created":"2018-02-17","cited_by_count":13},{"id":"2006.08580","title":"Uncertainty quantification for nonconvex tensor completion: Confidence intervals, heteroscedasticity and optimality","created":"2020-06-15","cited_by_count":13},{"id":"2104.12031","title":"Low-rank Tensor Estimation via Riemannian Gauss-Newton: Statistical Optimality and Second-Order Convergence","created":"2021-04-24","cited_by_count":13},{"id":"1711.01742","title":"Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA","created":"2017-11-06","cited_by_count":12},{"id":"2012.14844","title":"Inference for Low-rank Tensors -- No Need to Debias","created":"2020-12-29","cited_by_count":12},{"id":"1801.09326","title":"Sparse and Low-rank Tensor Estimation via Cubic Sketchings","created":"2018-01-29","cited_by_count":11},{"id":"2009.11282","title":"Learning Mixtures of Low-Rank Models","created":"2020-09-23","cited_by_count":11},{"id":"1611.01129","title":"Cross: Efficient Low-rank Tensor Completion","created":"2016-11-03","cited_by_count":10},{"id":"1709.08114","title":"Nonconvex Low-Rank Matrix Recovery with Arbitrary Outliers via Median-Truncated Gradient Descent","created":"2017-09-23","cited_by_count":10},{"id":"1911.03725","title":"Tensor Regression Using Low-rank and Sparse Tucker Decompositions","created":"2019-11-09","cited_by_count":10},{"id":"2006.16297","title":"Optimization Landscape of Tucker Decomposition","created":"2020-06-29","cited_by_count":10},{"id":"1711.04965","title":"Near-optimal sample complexity for convex tensor completion","created":"2017-11-14","cited_by_count":9},{"id":"1911.11167","title":"Manifold Gradient Descent Solves Multi-Channel Sparse Blind Deconvolution Provably and Efficiently","created":"2019-11-25","cited_by_count":6},{"id":"1707.02569","title":"On orthogonal tensors and best rank-one approximation ratio","created":"2017-07-09","cited_by_count":2},{"id":"1601.01469","title":"Tensor and Its Tucker Core: the Invariance Relationships","created":"2016-01-07","cited_by_count":1}],"next_cursor":null}