Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Group Fairness in Set Packing Problems
Authors: Sharmila Duppala, Juan Luque, John Dickerson, Aravind Srinivasan
IJCAI 2023 | Venue PDF | LLM Run Details | Input Tokens: 18,751 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,016 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL |
| 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]. |