Research map / Learning theory and algorithms / Sample complexity
Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels
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
- Fast Sketching of Polynomial Kernels of Polynomial Degree (ICML 2021 · similarity 0.90)
- Near Optimal Sketching of Low-Rank Tensor Regression (NeurIPS 2017 · similarity 0.88)
- Fast Sampling-Based Sketches for Tensors (ICML 2024 · similarity 0.87)
- Matrix Product Sketching via Coordinated Sampling (ICLR 2025 · similarity 0.86)
- Optimal Sketching for Residual Error Estimation for Matrix and Vector Norms (ICLR 2024 · similarity 0.85)
- Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit (NeurIPS 2024 · similarity 0.83)
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.