Low-Rank Structure in Machine Learning Reading List

Curated by Mouhssine Rifaki | Stanford Electrical Engineering | Last updated April 2026

Exploiting low-rank structure for efficiency and generalization in machine learning and reinforcement learning.

1. Exact Matrix Completion via Convex Optimization
   Candes and Recht. Found. Comput. Math 2009.
2. Matrix Completion from a Few Entries
   Keshavan, Montanari, Oh. IEEE Trans. IT 2010.
3. A Simpler Approach to Matrix Completion
   Recht. JMLR 2011.
4. CUR Matrix Decompositions for Improved Data Analysis
   Mahoney and Drineas. PNAS 2009.
5. Low-Rank Value Function Approximation for Co-optimization of Battery and Hybrid Microgrids
   Jiang et al.. IEEE 2017.
6. Spectral Methods for Data Science
   Chen, Fan, Ma, Yan. Cambridge 2024.
7. Entrywise Eigenvector Analysis of Random Matrices with Low Expected Rank
   Abbe, Fan, Wang, Zhong. Annals of Statistics 2020.
8. Implicit Regularization in Matrix Factorization
   Gunasekar et al.. NeurIPS 2017.
9. Recovering Low-Rank Matrices from Few Coefficients in Any Basis
   Gross. IEEE Trans. IT 2011.
10. Tensor and Matrix Low-Rank Value-Function Approximation in Reinforcement Learning
    Rozada et al.. arXiv 2022.