Research map / Optimization / Optimizers
Regularized Gradient Boosting
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace XGBoost-style GB with RGB (SRM-based complexity penalty and randomized coordinate search) | gradient boosted trees · sonar, cancer, ocr17, ocr49, mnist17, mnist49, higgs | test error %: sonar 28.64 -> 26.94; ocr17 1.35 -> 0.90; higgs 29.11 -> 28.60 | Helped |
| replace XGBoost-style GB with RGB | gradient boosted trees · diabetes | test error %: 28.39 -> 28.86 | No clear effect |
Closest papers by meaning
- Pushing the Envelope of Gradient Boosting Forests via Globally-Optimized Oblique Trees (CVPR 2022 · similarity 0.89)
- Statistical Inference for Gradient Boosting Regression (NeurIPS 2025 · similarity 0.88)
- Gradient Regularized Newton Boosting Trees with Global Convergence (NeurIPS 2026 · similarity 0.87)
- Uncertainty in Gradient Boosting via Ensembles (ICLR 2021 · similarity 0.87)
- SnapBoost: A Heterogeneous Boosting Machine (NeurIPS 2020 · similarity 0.87)
- Minimax Rates and Spectral Distillation for Tree Ensembles (NeurIPS 2026 · similarity 0.86)
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