Research map / Learning theory and algorithms / Kernels and regression theory
A Limitation of the PAC-Bayes Framework
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
- How Tight Can PAC-Bayes be in the Small Data Regime? (NeurIPS 2021 · similarity 0.91)
- PAC-Bayes Analysis Beyond the Usual Bounds (NeurIPS 2020 · similarity 0.90)
- Controlling Multiple Errors Simultaneously with a PAC-Bayes Bound (NeurIPS 2024 · similarity 0.90)
- Learning via Surrogate PAC-Bayes (NeurIPS 2024 · similarity 0.87)
- PAC-Bayes Bounds for Multivariate Linear Regression and Linear Autoencoders (NeurIPS 2025 · similarity 0.86)
- Recursive PAC-Bayes: A Frequentist Approach to Sequential Prior Updates with No Information Loss (NeurIPS 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.