Research map / Learning theory and algorithms / Sample complexity
Optimal Excess Risk Bounds for Empirical Risk Minimization on $p$-Norm Linear 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
- On Empirical Risk Minimization with Dependent and Heavy-Tailed Data (NeurIPS 2021 · similarity 0.88)
- All ERMs Can Fail in Stochastic Convex Optimization Lower Bounds in Linear Dimension (ICML 2026 · similarity 0.88)
- Stability and Sharper Risk Bounds with Convergence Rate $\tilde{O}(1/n^2)$ (NeurIPS 2025 · similarity 0.87)
- On the Efficiency of ERM in Feature Learning (NeurIPS 2024 · similarity 0.85)
- On the Asymptotic Distribution of the Minimum Empirical Risk (ICML 2024 · similarity 0.85)
- The Empirical Mean is Minimax Optimal for Local Glivenko-Cantelli (ICML 2025 · 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.
