Research map / Optimization / Distributed and compressed
Decentralized Matrix Sensing: Statistical Guarantees and Fast Convergence
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
- Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula (NeurIPS 2016 · similarity 0.88)
- Guarantees of a Preconditioned Subgradient Algorithm for Overparameterized Asymmetric Low-rank Matrix Recovery (ICML 2025 · similarity 0.86)
- Achieving Linear Convergence with Parameter-Free Algorithms in Decentralized Optimization (NeurIPS 2024 · similarity 0.86)
- Fundamental Limits of Distributed Covariance Matrix Estimation Under Communication Constraints (ICML 2024 · similarity 0.85)
- How Over-Parameterization Slows Down Gradient Descent in Matrix Sensing: The Curses of Symmetry and Initialization (ICLR 2024 · similarity 0.85)
- Implicit Regularization of Decentralized Gradient Descent for Sparse Regression (NeurIPS 2024 · similarity 0.85)
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.