Research map / Learning theory and algorithms / Kernels and regression theory
Margins are Insufficient for Explaining Gradient Boosting
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
- Revisiting Agnostic Boosting (NeurIPS 2025 · similarity 0.88)
- Statistical Inference for Gradient Boosting Regression (NeurIPS 2025 · similarity 0.88)
- Achieving Margin Maximization Exponentially Fast via Progressive Norm Rescaling (ICML 2024 · similarity 0.88)
- Input Margins Can Predict Generalization Too (AAAI 2024 · similarity 0.86)
- Gradient Regularized Newton Boosting Trees with Global Convergence (NeurIPS 2026 · similarity 0.86)
- Flavors of Margin: Implicit Bias of Steepest Descent in Homogeneous Neural Networks (ICLR 2025 · 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.
