Boosted Conformal Prediction Intervals
Authors: Ran Xie, Rina Barber, Emmanuel Candes
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Systematic experiments demonstrate that starting from conventional conformal methods, our boosted procedure achieves substantial improvements in reducing interval length and decreasing deviation from target conditional coverage. |
| Researcher Affiliation | Academia | Ran Xie Department of Statistics Stanford University ranxie@stanford.edu Rina Foygel Barber Department of Statistics University of Chicago rina@uchicago.edu Emmanuel J. Cand es Department of Statistics Department of Mathematics Stanford University candes@stanford.edu |
| Pseudocode | Yes | Algorithm 1 Boosting stage |
| Open Source Code | Yes | Source code for implementing the boosted conformal procedure is available online at https://github.com/ran-xie/boosted-conformal. |
| Open Datasets | Yes | All datasets, except for the meps and star data sets, are licensed under CC-BY 4.0. The Medical Expenditure Panel Survey (meps) data is subject to copyright and usage rules. The licensing status of the star dataset could not be determined. |
| Dataset Splits | Yes | The number τ is calculated using k-fold cross-validation on the training dataset, selecting τ from potential values up to a predefined maximum T (e.g., 500). We partition the dataset into k folds and for each j = 1, . . . , k, we hold out fold j for sub-calibration and the remaining k 1 folds for sub-training. |
| Hardware Specification | Yes | All experiments were conducted on a dual-socket AMD EPYC 7502 32-Core Processor system, utilizing 8 of its 128 CPUs each time. |
| Software Dependencies | No | The paper mentions 'Python s scikit-learn package' and 'Python s XGBoost package' but does not specify exact version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We set the hyper-parameters τ1, τ2 in the approximated loss (17) to 50. The approximated loss is then passed to the Gradient Boosting Machine from Python s XGBoost package along with a base conformity score. We set the maximum tree depth to 1 to avoid overfitting and perform cross-validation for the number of boosting rounds, as outlined in Section 3. |