Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Accurate Uncertainty Estimation and Decomposition in Ensemble Learning
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 |
|---|---|---|---|
| Add calibration function G to flexibly correct the predictive distribution (ablation) | BNE · Synthetic nonlinear function approximation | L1 distance to true CDF | Helped |
| Add calibration function G to flexibly correct the predictive distribution (ablation) | BNE · Synthetic nonlinear function approximation | RMSE | Helped |
| Add residual process δ to correct prediction-function bias (ablation) | BNE · Synthetic nonlinear function approximation | RMSE | Helped |
| Add residual process δ to represent structural uncertainty away from observed data (ablation) | BNE · Synthetic nonlinear function approximation | Predictive uncertainty | Helped |
| Use the full BNE rather than Bayesian Stacking or BAE | Ensemble methods · Synthetic nonlinear function approximation | RMSE | Helped |
| Use the full BNE rather than Bayesian Stacking or BAE | Ensemble methods · Synthetic nonlinear function approximation | L1 distance to true CDF | Helped |
| Use DeepEns instead of BNE in the low-data regime | Synthetic nonlinear function approximation · Small sample sizes | Generalization performance | Hurt |
| Use BNE rather than CondKDE | Synthetic nonlinear function approximation · Time series simulation | Prediction and uncertainty estimation | No clear effect |
Closest papers by meaning
- Toward Robust Uncertainty Estimation with Random Activation Functions (AAAI 2023 · similarity 0.87)
- Uncertainty Quantification with the Empirical Neural Tangent Kernel (NeurIPS 2025 · similarity 0.85)
- Quantifying Uncertainty in the Presence of Distribution Shifts (NeurIPS 2025 · similarity 0.84)
- Efficient Analytic Uncertainty Quantification for Multimodal Regression (NeurIPS 2026 · similarity 0.84)
- Estimating Epistemic and Aleatoric Uncertainty with a Single Model (NeurIPS 2024 · similarity 0.84)
- Structure-informed Risk Minimization for Robust Ensemble Learning (ICML 2025 · similarity 0.83)
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.