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..
The Price of Opportunity Fairness in Matroid Allocation Problems
Authors: Rémi Castera, Felipe Garrido-Lucero, Patrick Loiseau, Simon Mauras, Mathieu Molina, Vianney Perchet
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We first provide a characterization of the Po F leveraging the underlying polymatroid structure of the allocation problem. Based on this characterization, we prove bounds on the Po F in various settings from fully adversarial (worst-case) to fully random. Notably, one of our main results considers an arbitrary matroid structure with agents randomly divided into groups. In this setting, we prove a Po F bound as a function of the (relative) size of the largest group. Overall, our results give insights into which aspects of the problem s structure affect the trade-off between opportunity fairness and social welfare. |
| Researcher Affiliation | Collaboration | Rémi Castera Moroccan Center for Game Theory University Mohammed VI Polytechnic Rabat, Morocco Felipe Garrido-Lucero IRIT, Université Toulouse Capitole Toulouse, France Patrick Loiseau Inria, Fairplay joint team Palaiseau, France Simon Mauras Inria, Fairplay joint team Palaiseau, France Mathieu Molina Inria, Fairplay joint team Crest, ENSAE Palaiseau, France Vianney Perchet ENSAE, Fairplay joint team Criteo AI Lab Palaiseau, France |
| Pseudocode | No | The paper describes algorithmic steps and computations in prose, such as in Section D "Efficient computation of Opportunity fair allocations of maximum size," but does not include any explicitly labeled pseudocode blocks or algorithms. |
| Open Source Code | No | 5. Open access to data and code Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [NA] Justification: |
| Open Datasets | No | The paper does not include experiments or refer to any specific publicly available datasets for empirical validation. The NeurIPS checklist indicates 'NA' for questions related to experimental reproducibility and data access. |
| Dataset Splits | No | The paper does not include experiments or refer to any specific datasets with described splits. The NeurIPS checklist indicates 'NA' for questions related to experimental reproducibility and data access. |
| Hardware Specification | No | The paper does not include experiments, and therefore no hardware specifications are provided. The NeurIPS checklist indicates 'NA' for questions related to experimental compute resources. |
| Software Dependencies | No | The paper does not include experiments, and therefore no specific software dependencies with version numbers are listed. The NeurIPS checklist indicates 'NA' for questions related to experimental reproducibility. |
| Experiment Setup | No | The paper does not include experiments, and therefore no experimental setup details, including hyperparameters or training configurations, are provided. The NeurIPS checklist indicates 'NA' for questions related to experimental settings and reproducibility. |