Improving Screening Processes via Calibrated Subset Selection
Authors: Lequn Wang, Thorsten Joachims, Manuel Gomez Rodriguez
ICML 2022 | Conference PDF | Archive PDF | Plain Text | 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. |