Research map / Learning theory and algorithms / Matrix and tensor methods
Few-Shot Data-Driven Algorithms for Low Rank Approximation
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
- Learning-Based Low-Rank Approximations (NeurIPS 2019 · similarity 0.93)
- Tensor-Based Sketching Method for the Low-Rank Approximation of Data Streams. (ICLR 2023 · similarity 0.90)
- Learning the Positions in CountSketch (ICLR 2023 · similarity 0.89)
- Reweighted Solutions for Weighted Low Rank Approximation (ICML 2024 · similarity 0.87)
- Low Rank Matrix Completion via Robust Alternating Minimization in Nearly Linear Time (ICLR 2024 · similarity 0.86)
- Efficient Alternating Minimization with Applications to Weighted Low Rank Approximation (ICLR 2025 · similarity 0.86)
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.