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..
Internally Stable Matchings and Exchanges
Authors: Yicheng Liu, Pingzhong Tang, Wenyi Fang
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on the kidney exchange domain demonstrate that the optimal welfare under internal stability is very close to the unconstrained optimal. |
| Researcher Affiliation | Academia | Yicheng Liu, Pingzhong Tang and Wenyi Fang Institute of Interdisciplinary Information Sciences, Tsinghua University, Beijing, China |
| Pseudocode | Yes | Algorithm 1: Cycle reduction algorithm |
| Open Source Code | No | Of independent interests, we include in the full version our ILP implementation for computing 3-2 exchanges. |
| Open Datasets | Yes | Our data generator is carefully designed based on statistics of the US and China populations.(Tan, Zhou, and Tang 2006; Tu, Chen, and Wang 2005; Segev et al. 2005; Zhang 2004). |
| Dataset Splits | No | The paper mentions using 'data sets generated by the US and China population statistics' but does not provide specific details on train/validation/test splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory specifications) used for the experiments. |
| Software Dependencies | No | The paper mentions 'CPLEX' but does not provide a specific version number or other software dependencies with versions. |
| Experiment Setup | No | The paper does not explicitly provide details about the experimental setup such as hyperparameters, model initialization, or training schedules. |