Research map / Learning theory and algorithms / Matrix and tensor methods
Rank Overspecified Robust Matrix Recovery: Subgradient Method and Exact Recovery
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Guarantees of a Preconditioned Subgradient Algorithm for Overparameterized Asymmetric Low-rank Matrix Recovery (ICML 2025 · similarity 0.93)
- Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization (NeurIPS 2020 · similarity 0.90)
- A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery (ICML 2017 · similarity 0.90)
- Robust Matrix Sensing in the Semi-Random Model (NeurIPS 2023 · similarity 0.90)
- RGNMR: A Gauss-Newton method for robust matrix completion with theoretical guarantees (NeurIPS 2025 · similarity 0.89)
- LERE: Learning-Based Low-Rank Matrix Recovery with Rank Estimation (AAAI 2024 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.