Research map / Learning theory and algorithms / Kernels and regression theory
The Exact Sample Complexity Gain from Invariances for Kernel Regression
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
- Sample Complexity Bounds for Estimating Probability Divergences under Invariances (ICML 2024 · similarity 0.90)
- Learning with Exact Invariances in Polynomial Time (ICML 2025 · similarity 0.90)
- A Comprehensive Analysis on the Learning Curve in Kernel Ridge Regression (NeurIPS 2024 · similarity 0.86)
- Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying Bandwidth or Dimensionality (NeurIPS 2024 · similarity 0.86)
- On the Saturation Effects of Spectral Algorithms in Large Dimensions (NeurIPS 2024 · similarity 0.85)
- Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum (ICML 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.
