Exploring Algorithmic Fairness in Robust Graph Covering Problems
Authors: Aida Rahmattalabi, Phebe Vayanos, Anthony Fulginiti, Eric Rice, Bryan Wilder, Amulya Yadav, Milind Tambe
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our approach on several real-world social networks. Our method yields competitive node coverage while significantly improving group fairness relative to state-of-the-art methods. |
| Researcher Affiliation | Academia | University of Southern California University of Denver Harvard University Pennsylvania State University |
| Pseudocode | No | The paper does not include pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide a statement about releasing open-source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | We evaluate our approach on the five social networks from Table 1. Details on the data are provided in Section A. ... social networks from two homeless drop-in centers in Los Angeles, CA [4]. [4] Anamika Barman-Adhikari, Stephanie Begun, Eric Rice, Amanda Yoshioka-Maxwell, and Andrea Perez-Portillo. Sociometric network structure and its association with methamphetamine use norms among homeless youth. Social science research, 58:292 308, 2016. |
| Dataset Splits | No | The paper does not specify training, validation, and test dataset splits, nor does it refer to predefined splits with citations or cross-validation setups. |
| Hardware Specification | No | The paper mentions "All experiments were ran on a Linux 16GB RAM machine" but does not specify CPU/GPU models or other specific hardware components. |
| Software Dependencies | Yes | All experiments were ran on a Linux 16GB RAM machine with Gurobi v6.5.0. |
| Experiment Setup | Yes | We set a time limit of 2 hours since little improvement was seen beyond that. ... In all cases, and in particular for K = 2 and 3, symmetry breaking results in significant speed-ups. ... For K = 3 (and contrary to Bender s decomposition augmented with symmetry breaking), Bender s decomposition alone fails to solve the master problem to optimality within the time limit. |