Research map / Learning theory and algorithms / Kernels and regression theory
Dense-Exponential Random Features: Sharp Positive Estimators of the Gaussian Kernel
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
- Chefs' Random Tables: Non-Trigonometric Random Features (NeurIPS 2022 · similarity 0.89)
- Hybrid Random Features (ICLR 2022 · similarity 0.87)
- Gaussian Quadrature for Kernel Features (NeurIPS 2017 · similarity 0.87)
- Variance-Reducing Couplings for Random Features (ICLR 2025 · similarity 0.85)
- Sparse Attention as Compact Kernel Regression (NeurIPS 2026 · similarity 0.84)
- Optimal Kernel Quantile Learning with Random Features (ICML 2024 · similarity 0.84)
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.
