Papers
12 papers found
Algorithmic Regularization in Tensor Optimization: Towards a Lifted Approach in Matrix Sensing
Ziye Ma, Javad Lavaei, Somayeh Sojoudi
Decentralized Matrix Sensing: Statistical Guarantees and Fast Convergence
Marie Maros, Gesualdo Scutari
Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing
Shuyao Li, Yu Cheng, Ilias Diakonikolas et al.
Greedy Pruning with Group Lasso Provably Generalizes for Matrix Sensing
Nived Rajaraman, Fnu Devvrit, Aryan Mokhtari et al.
Geometric Analysis of Matrix Sensing over Graphs
Haixiang Zhang, Ying Chen, Javad Lavaei
Robust Matrix Sensing in the Semi-Random Model
Xing Gao, Yu Cheng
Semidefinite Programming versus Burer-Monteiro Factorization for Matrix Sensing
Baturalp Yalçın, Ziye Ma, Javad Lavaei et al.
Implicit Balancing and Regularization: Generalization and Convergence Guarantees for Overparameterized Asymmetric Matrix Sensing
Mahdi Soltanolkotabi, Dominik Stöger, Changzhi Xie
Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing
Jikai Jin, Zhiyuan Li, Kaifeng Lyu et al.
Over-parametrization via Lifting for Low-rank Matrix Sensing: Conversion of Spurious Solutions to Strict Saddle Points
Ziye Ma, Igor Molybog, Javad Lavaei et al.
The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing
Xingyu Xu, Yandi Shen, Yuejie Chi et al.
Lower Bounds on Adaptive Sensing for Matrix Recovery
Praneeth Kacham, David Woodruff