Matchings with Group Fairness Constraints: Online and Offline Algorithms
Authors: Govind S. Sankar, Anand Louis, Meghana Nasre, Prajakta Nimbhorkar
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present the experimental evaluation of our offline algorithms from Theorem 1 and Theorem 2. We use a total of seven datasets which we categorize as real-world and synthetic datasets. |
| Researcher Affiliation | Academia | 1Indian Institute of Technology Madras, Chennai 2Indian Institute of Science, Bangalore 3Chennai Mathematical Institute, Chennai 4UMI Re La X |
| Pseudocode | No | The paper describes algorithmic steps within the text and proofs, but does not include any formally structured pseudocode blocks or algorithm listings. |
| Open Source Code | No | The paper states 'All code was written to run on Python 3.8' but does not provide any link or explicit statement about the release of its own source code for the described methodology. |
| Open Datasets | No | The paper mentions using real-world datasets from an educational institution and synthetically generated datasets, but it does not provide concrete access information (e.g., specific links, DOIs, or formal citations to publicly available repositories) for either type of dataset. |
| Dataset Splits | No | The paper does not provide specific details on dataset splits (e.g., percentages, sample counts, or citations to predefined splits) used for training, validation, or testing in its experiments. |
| Hardware Specification | Yes | All experiments were run on a laptop running on a 64-bit Windows 10 Home edition, and equipped with an Intel Core i7-7500U CPU @2.7GHz and 12GB of RAM. |
| Software Dependencies | Yes | For solving integer programs, we used IBM ILOG CPLEX Optimization Studio 20.1 through its Python API. All code was written to run on Python 3.8. |
| Experiment Setup | No | The paper describes aspects of dataset generation and the use of an Integer Linear Program solver, but it does not provide specific hyperparameter values or detailed system-level training configurations for the algorithms. |