Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Predictive inference is free with the jackknife+-after-bootstrap
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 |
|---|---|---|---|
| Reuse fitted ensemble models to construct out-of-bag leave-one-out predictions | Ensemble predictive intervals · n_test predictions | calls to base regression method: $B n$ -> $B$ | Helped |
| Use JaB instead of jackknife+ wrapped around the ensemble | Ensemble predictive intervals · Communities and Crime, BlogFeedback, and MEPS | interval width | No clear effect |
| Bag the base learner using JaB instead of using non-ensembled jackknife+ | Random forest predictive intervals · Communities and Crime, BlogFeedback, and MEPS | interval width | Helped |
| Bag the base learner using JaB instead of using non-ensembled jackknife+ | Neural-network predictive intervals · Some data sets and some values of m | interval width | Mixed |
| Use JaB with the tested ensemble settings | JaB · Communities and Crime, BlogFeedback, and MEPS | coverage | No clear effect |
| Use a fixed B instead of the Binomial draw | JaB · Distribution-free prediction | coverage | Mixed |
| Add ensemble stability and inflate the JaB interval by 2\varepsilon | JaB · Fixed B | coverage lower bound: 1 - 2\alpha -> 1 - 2\alpha - 4\sqrt{\delta} | Helped |
| Add ensemble stability and average algorithmic stability, with interval inflation | JaB · Fixed B | coverage lower bound: 1 - \alpha - 3\sqrt{\delta} - 4\sqrt{\delta^*} | Helped |
Closest papers by meaning
- Orthogonal Bootstrap: Efficient Simulation of Input Uncertainty (ICML 2024 · similarity 0.84)
- Kernel conditional tests from learning-theoretic bounds (NeurIPS 2025 · similarity 0.83)
- Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning (NeurIPS 2024 · similarity 0.83)
- Conformal Prediction for Ensembles: Improving Efficiency via Score-Based Aggregation (NeurIPS 2025 · similarity 0.82)
- Spend Wisely: Maximizing Post-Training Gains in Iterative Synthetic Data Bootstrapping (NeurIPS 2025 · similarity 0.82)
- Self-Calibrating Conformal Prediction (NeurIPS 2024 · similarity 0.82)
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.