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..
Improving Screening Processes via Calibrated Subset Selection
Authors: Lequn Wang, Thorsten Joachims, Manuel Gomez Rodriguez
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on US Census survey data validate our theoretical results and show that the shortlists provided by our algorithm are superior to those provided by several competitive baselines. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Cornell University 2Most of the work was done during Wang s internship at the Max Planck Institute for Software Systems. 3Max Planck Institute for Software Systems. |
| Pseudocode | Yes | Algorithm 1 Calibrated Subset Selection (CSS) |
| Open Source Code | Yes | Our code is accessible at https://github.com/Lequn Wang/Improve-Screening-via Calibrated-Subset-Selection. |
| Open Datasets | Yes | We create a simulated screening process using a dataset comprised of employment information for 3.2 million individuals from the US Census (Ding et al., 2021). |
| Dataset Splits | No | The paper mentions using a 'training set' and 'calibration sets' and a 'test pool of candidates'. However, it does not explicitly describe a separate 'validation' split (e.g., for hyperparameter tuning) with specific percentages or counts. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., GPU models, CPU types, or cloud instance specifications). |
| Software Dependencies | No | The paper mentions training a 'logistic regression' classifier but does not specify any software names with version numbers (e.g., Python, scikit-learn, PyTorch, TensorFlow versions) that were used. |
| Experiment Setup | Yes | In each simulated screening process, we set the size of the test pool of candidates to m = 100, the desired expected number of qualified candidates to k = 5, and the success probability to 1 α = 0.9. For the diversity experiments, we set the desired expected number of qualified candidates kmaj and kmin so that the equal opportunity constraint... is satisfied subject to kmaj + kmin = 5. |