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1704.00708
Cited By
No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis
3 April 2017
Rong Ge
Chi Jin
Yi Zheng
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Papers citing
"No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis"
50 / 228 papers shown
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186
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Nonnegative Low-rank Matrix Recovery Can Have Spurious Local Minima
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171
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Euclidean Distance Matrix Completion via Asymmetric Projected Gradient Descent
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268
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28 Apr 2025
Preconditioned Gradient Descent for Over-Parameterized Nonconvex Matrix Factorization
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Yassir Jedra
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David A. Knowles
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313
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Aryan Mokhtari
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418
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Liang Zhang
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296
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18 Oct 2024
On the Learn-to-Optimize Capabilities of Transformers in In-Context Sparse Recovery
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Renpu Liu
Ruida Zhou
Cong Shen
Jing Yang
513
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280
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316
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Federated Representation Learning in the Under-Parameterized Regime
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Cong Shen
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356
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Peng Wang
Laura Balzano
Qing Qu
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246
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A Global Geometric Analysis of Maximal Coding Rate Reduction
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Peng Wang
Huikang Liu
Druv Pai
Yaodong Yu
Zhihui Zhu
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299
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Low solution rank of the matrix LASSO under RIP with consequences for rank-constrained algorithms
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252
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Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing
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Shuyao Li
Yu Cheng
Ilias Diakonikolas
Jelena Diakonikolas
Rong Ge
Stephen J. Wright
238
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Absence of spurious solutions far from ground truth: A low-rank analysis with high-order losses
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Ziye Ma
Ying Chen
Javad Lavaei
Somayeh Sojoudi
236
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10 Mar 2024
Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape
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Taiji Suzuki
378
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Learning Rich Rankings
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Stephen Ragain
J. Ugander
215
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Wave Physics-informed Matrix Factorizations
Harsha Vardhan Tetali
J. Harley
B. Haeffele
327
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High Probability Guarantees for Random Reshuffling
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Xiao Li
301
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20 Nov 2023
A randomized algorithm for nonconvex minimization with inexact evaluations and complexity guarantees
Shuyao Li
Stephen J. Wright
249
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28 Oct 2023
Stochastic Optimization for Non-convex Problem with Inexact Hessian Matrix, Gradient, and Function
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Liu Liu
Xuanqing Liu
Cho-Jui Hsieh
Dacheng Tao
166
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Xingyu Xu
Tian Tong
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308
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How Over-Parameterization Slows Down Gradient Descent in Matrix Sensing: The Curses of Symmetry and Initialization
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Nuoya Xiong
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Simon S. Du
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Global Optimality in Bivariate Gradient-based DAG Learning
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Kevin Bello
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Last-Iterate Convergence of Adaptive Riemannian Gradient Descent for Equilibrium Computation
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Michael I. Jordan
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Argyris Oikonomou
Emmanouil-Vasileios Vlatakis-Gkaragkounis
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Bootstrapped Representations in Reinforcement Learning
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Charline Le Lan
Stephen Tu
Mark Rowland
Anna Harutyunyan
Rishabh Agarwal
Marc G. Bellemare
Will Dabney
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262
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16 Jun 2023
The Law of Parsimony in Gradient Descent for Learning Deep Linear Networks
Can Yaras
Peng Wang
Wei Hu
Zhihui Zhu
Laura Balzano
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308
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01 Jun 2023
Statistically Optimal K-means Clustering via Nonnegative Low-rank Semidefinite Programming
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Yubo Zhuang
Xiaohui Chen
Yun Yang
Richard Y. Zhang
363
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Personalized Dictionary Learning for Heterogeneous Datasets
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Geyu Liang
Naichen Shi
Raed Al Kontar
Salar Fattahi
208
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Accelerated Algorithms for Nonlinear Matrix Decomposition with the ReLU function
International Workshop on Machine Learning for Signal Processing (MLSP), 2023
Giovanni Seraghiti
Atharva Awari
A. Vandaele
M. Porcelli
Nicolas Gillis
115
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Greedy Pruning with Group Lasso Provably Generalizes for Matrix Sensing
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Nived Rajaraman
Devvrit
Aryan Mokhtari
Kannan Ramchandran
258
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Can Learning Be Explained By Local Optimality In Robust Low-rank Matrix Recovery?
Mathematics of Operations Research (MOR), 2023
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Salar Fattahi
452
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Efficient displacement convex optimization with particle gradient descent
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Hadi Daneshmand
Jason D. Lee
Chi Jin
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Approximate message passing from random initialization with applications to
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Proceedings of the National Academy of Sciences of the United States of America (PNAS), 2023
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Wei Fan
Yuting Wei
297
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Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing
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Zhiyuan Li
Kaifeng Lyu
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