Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Selection by Prediction with Conformal p-values
Authors: Ying Jin, Emmanuel J. Candes
JMLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the empirical performance of our method via simulations, and apply it to job hiring and drug discovery datasets. |
| Researcher Affiliation | Academia | Ying Jin EMAIL Department of Statistics Stanford University Stanford, CA 94305, USA Emmanuel J. Candes EMAIL Department of Statistics and Department of Mathematics Stanford University Stanford, CA 94305, USA |
| Pseudocode | Yes | The whole procedure for cf BH is summarized in Algorithm 1. Algorithm 1 cf BH: Selection by prediction with conformal p-values Algorithm 2 cf BH0: Selection by prediction with same-class calibration |
| Open Source Code | Yes | The reproduction codes for this part can be found at https://github.com/ying531/selcf_paper. |
| Open Datasets | Yes | We use a small-scale recruitment dataset from Kaggle (Roshan, 2020), as recruitment datasets from companies are often confidential. We use the DAVIS dataset published in Davis et al. (2011), which records real-valued binding affinities for ntot = 30060 drug-target pairs. |
| Dataset Splits | Yes | We randomly split the data into a training set of size |Dtrain| = 86 and a test set of size |Dtest| = 43. We randomly split the data into three folds with ratio 6 : 2 : 2 in size. In particular, we randomly split the dataset into three folds of size 2 : 2 : 6 |
| Hardware Specification | No | We train a small neural network in only 3 epochs so that the whole procedure works well with CPUs; We train a small neural network over 10 epochs. These choices are suitable for experiments on CPUs (one might of course use other more computationally intensive alternatives). |
| Software Dependencies | No | We use gradient boosting, SVM with rbf kernel, and random forest to fit a regression model bµ( ) for E[Y | X], all from the scikit-learn Python library without fine tuning. prediction pipelines established in the Deep Purpose library (Huang et al., 2020). |
| Experiment Setup | Yes | We train a small neural network in only 3 epochs so that the whole procedure works well with CPUs; We train a small neural network over 10 epochs. We use gradient boosting, SVM with rbf kernel, and random forest to fit a regression model bµ( ) for E[Y | X], all from the scikit-learn Python library without fine tuning. |