Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Rawlsian Fairness in Online Bipartite Matching: Two-Sided, Group, and Individual
Authors: Seyed Esmaeili, Sharmila Duppala, Davidson Cheng, Vedant Nanda, Aravind Srinivasan, John P. Dickerson
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically test our algorithms on a real-world dataset. In this section, we verify the performance of our algorithm and our theoretical lower bounds for the KIID and group fairness setting using algorithm TSGFKIID (Section 5.1). |
| Researcher Affiliation | Academia | 1 University of Maryland, College Park 2 Colorado College |
| Pseudocode | Yes | Algorithm 1: PPDR( xv) Algorithm 2: TSGFKIID(α, β, γ) Algorithm 3: TSGFKAD(α, β, γ) |
| Open Source Code | Yes | Code to reproduce our experiments is available in the blinded format ; we will release that code in deblinded form upon acceptance. https://github.com/anonymousUser634534/TSGF |
| Open Datasets | Yes | We run our experiments over the widely used New York City (NYC) yellow cabs dataset (Sekuli c, Long, and Demˇsar 2021; Nanda et al. 2020; Xu and Xu 2020; Alonso-Mora, Wallar, and Rus 2017) which contains records of taxi trips in the NYC area from 2013. |
| Dataset Splits | No | The paper describes using the NYC dataset and running trials but does not specify explicit train/validation/test splits or their percentages/counts for reproducibility. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU/CPU models, memory, or cloud resources used for the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming language versions, library versions, or solver versions). |
| Experiment Setup | Yes | We pick out the trips between 7pm and 8pm on January 31, 2013, which is a rush hour with 10,814 drivers and 35,109 trips. We set driver patience u to 3. Following (Xu and Xu 2020), we uniformly sample rider patience v from {1, 2}. Specifically, we randomly assign 70% of the riders and drivers to be advantaged and the rest to be disadvantaged. The value of pe for e = (u, v) depends on whether the vertices belong to the advantaged or disadvantaged group. Specifically, pe = 0.6 if both vertices are advantaged, pe = 0.3 if both are disadvantaged, and pe = 0.1 for other cases. We run TSGFKIID at the scale of |U| = 49, |V | = 172 for 100 trials. |