Group Fairness in Set Packing Problems
Authors: Sharmila Duppala, Juan Luque, John Dickerson, Aravind Srinivasan
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conclude with experiments on synthetic and realistic kidney exchange FAIRSP instances. |
| Researcher Affiliation | Academia | Sharmila Duppala , Juan Luque , John Dickerson and Aravind Srinivasan University of Maryland, College Park {sduppala, jluque, johnd, asriniv1}@umd.edu |
| Pseudocode | Yes | Algorithm 1 FAIRSAMPLE |
| Open Source Code | No | The paper does not explicitly state that the code for its methodology is open-source or provide a link to a code repository. |
| Open Datasets | Yes | We use realistic kidney exchange instances drawn from the US-based United Network for Organ Sharing (UNOS) fielded exchange program; our synthetic instances are generated from this data in the standard way [Dickerson et al., 2019]. |
| Dataset Splits | No | The paper describes how instances are generated and experiments are conducted across different graph sizes but does not specify explicit train/validation/test dataset splits. |
| Hardware Specification | No | Our experiments run on commodity hardware using Python 3.6 with Num Py [Harris et al., 2020] as well as IP and LP solvers in Gurobi 9.1.2. |
| Software Dependencies | Yes | Our experiments run on commodity hardware using Python 3.6 with Num Py [Harris et al., 2020] as well as IP and LP solvers in Gurobi 9.1.2. |
| Experiment Setup | Yes | FAIRSAMPLE uses L = 300 to estimate q i values. Experiments in Figure 3 use FAIRELIMINATE to round the 960 LPs with fairness constraints with 25 evenly spaced choices of the parameter a [0.1, 0.99]. |