Research map / Learning theory and algorithms / Kernels and regression theory
Towards a Unified Analysis of Kernel-based Methods Under Covariate Shift
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
- Computational Efficiency under Covariate Shift in Kernel Ridge Regression (NeurIPS 2025 · similarity 0.93)
- High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization (ICML 2024 · similarity 0.88)
- Sharp Generalization for Nonparametric Regression by Over-Parameterized Neural Networks: A Distribution-Free Analysis in Spherical Covariate (ICML 2025 · similarity 0.87)
- On Statistical Learning Theory for Distributional Inputs (ICML 2024 · similarity 0.86)
- Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift (ICML 2024 · similarity 0.86)
- Generalization in Kernel Regression Under Realistic Assumptions (ICML 2024 · 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.
