Research map / Learning theory and algorithms / Prediction and decision losses
Data Aware and Scalable Sensitivity Analysis for Decision Tree Ensembles
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
- Sensitivity Verification for Additive Decision Tree Ensembles (ICLR 2025 · similarity 0.93)
- Versatile Verification of Tree Ensembles (ICML 2021 · similarity 0.90)
- Multistage Defer Trees for Hybrid Interpretability: If at First You Can't Succeed, Tree Again (NeurIPS 2026 · similarity 0.89)
- On Computing Optimal Tree Ensembles (ICML 2023 · similarity 0.89)
- Compressing tree ensembles through Level-wise Optimization and Pruning (ICML 2025 · similarity 0.88)
- Robustness Verification of Multi-Class Tree Ensembles (AAAI 2024 · similarity 0.87)
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.