Centralized Selection with Preferences in the Presence of Biases
Authors: L. Elisa Celis, Amit Kumar, Nisheeth K. Vishnoi, Andrew Xu
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Further, extensive empirical validation of these results in real-world and synthetic settings, in which the distributional assumptions may not hold, are presented. |
| Researcher Affiliation | Academia | 1Yale University 2IIT Delhi. |
| Pseudocode | Yes | In this section, we provide the pseudocodes for the three algorithms we compare in Section 4, as well as extensions for relaxed bounds in Appendix G. These include a special case of the Gale-Shapley algorithm (Ast; Algorithm 1), the algorithm to satisfy group-wise proportional representation constraints (Agroup; Algorithm 2), the algorithm to satisfy institution-wise constraints (Ainst-wise; Algorithm 3), and modifications of Algorithm 2 and Algorithm 3 under relaxed bounds. |
| Open Source Code | Yes | The code and data can be found here1. 1https://github.com/sandrewxu/Centralized Selectionwith Preference Bias |
| Open Datasets | Yes | The data contains the test scores of 384,977 students from IITJEE 2009, self-reported gender and official birth category, opening and closing ranks, and capacities of major-institute pairs (JOSAA, 2009). |
| Dataset Splits | No | The paper describes simulation setups and iterations for evaluation but does not specify training, validation, or test dataset splits in the context of machine learning model training. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud resources) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | The paper states, 'We implement algorithms and run all empirical work in Python3,' but does not provide specific version numbers for Python or any other software dependencies or libraries. |
| Experiment Setup | Yes | Setup. We fix n = 1000, p = 5, and ki = 100 for i [p]. For D {DGauss, DPareto} and β { 1/4}, we vary γ [0, γmax] and calculate P(1) and U over 50 iterations. |