Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Gradient Boosting Performs Gaussian Process Inference
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace SGB/SGLB posterior-variance estimation with KGB Monte-Carlo posterior sampler | gradient boosted decision trees · 10 regression datasets (OOD detection) | AUC-ROC / PRR | Helped |
| Replace SGB/SGLB with KGB sampler | gradient boosted decision trees · 10 regression datasets (single-model RMSE) | RMSE | Helped |
| Replace SGB/SGLB with KGB sampler for error detection | gradient boosted decision trees · Naval (PRR) | PRR: KGB 35 vs SGB 55 | Hurt |
| Replace SGB/SGLB with KGB sampler for OOD detection | gradient boosted decision trees · Energy and Kin8nm and Naval (AUC) | AUC: Energy 99 vs 100; Kin8nm 41 vs 45; Naval equal 100 | Mixed |
Closest papers by meaning
- Statistical Inference for Gradient Boosting Regression (NeurIPS 2025 · similarity 0.88)
- Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning (NeurIPS 2024 · similarity 0.87)
- Uncertainty in Gradient Boosting via Ensembles (ICLR 2021 · similarity 0.87)
- Gradient Regularized Newton Boosting Trees with Global Convergence (NeurIPS 2026 · similarity 0.86)
- Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent (NeurIPS 2023 · similarity 0.86)
- SnapBoost: A Heterogeneous Boosting Machine (NeurIPS 2020 · similarity 0.86)
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