Research map / Learning theory and algorithms / Matrix and tensor methods
Decentralized sketching of low rank matrices
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.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use mixed-norm regularization instead of nuclear norm (matrix LASSO) | decentralized column-wise sketching recovery · simulated 1000x1000 rank-5 matrices, varying compression and SNR | relative estimation error | Helped |
Closest papers by meaning
- Optimal Sketching for Residual Error Estimation for Matrix and Vector Norms (ICLR 2024 · similarity 0.90)
- Matrix Product Sketching via Coordinated Sampling (ICLR 2025 · similarity 0.86)
- Distributed Least Squares in Small Space via Sketching and Bias Reduction (NeurIPS 2024 · similarity 0.86)
- Low Rank Matrix Completion via Robust Alternating Minimization in Nearly Linear Time (ICLR 2024 · similarity 0.86)
- Symmetric Matrix Completion with ReLU Sampling (ICML 2024 · similarity 0.85)
- RGNMR: A Gauss-Newton method for robust matrix completion with theoretical guarantees (NeurIPS 2025 · 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.