Research map / Learning theory and algorithms / Prediction and decision losses
Optimal Sparse Regression Trees
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.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Remove the maximum depth constraint (ablation) | OSRT · Servo | R²: 69.63% -> 75% | Helped |
| Use the k-Means lower bound instead of the equivalent-points lower bound (ablation) | OSRT · Airfoil | time to converge: less than one-fourth the time | Helped |
Closest papers by meaning
- Fast Sparse Decision Tree Optimization via Reference Ensembles (AAAI 2022 · similarity 0.88)
- Piecewise Constant and Linear Regression Trees: An Optimal Dynamic Programming Approach (ICML 2024 · similarity 0.88)
- Near-Optimal Decision Trees in a SPLIT Second (ICML 2025 · similarity 0.88)
- Branches: Efficiently Seeking Optimal Sparse Decision Trees via AO* (ICML 2025 · similarity 0.86)
- Minimax Rates and Spectral Distillation for Tree Ensembles (NeurIPS 2026 · similarity 0.86)
- Optimal Classification Trees for Continuous Feature Data Using Dynamic Programming with Branch-and-Bound (AAAI 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.