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..
Equitable Stable Matchings in Quadratic Time
Authors: Nikolaos Tziavelis, Ioannis Giannakopoulos, Katerina Doka, Nectarios Koziris, Panagiotis Karras
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental study with simulated markets shows that DLS outperforms the state of the art in equity measures and matches the most ef๏ฌcient ones in runtime. |
| Researcher Affiliation | Academia | Nikolaos Tziavelis Northeastern University Ioannis Giannakopoulos NTU Athens Katerina Doka NTU Athens Nectarios Koziris NTU Athens Panagiotis Karras Aarhus University |
| Pseudocode | Yes | Algorithm 1 Power Balance |
| Open Source Code | Yes | Code and data are available at https://github.com/ntzia/stable-marriage |
| Open Datasets | Yes | We use synthetic datasets that draw preferences from three distributions: Uniform(U), Discrete(D), and Gaussian(G)... Last, we apply our solution on real data... Code and data are available at https://github.com/ntzia/stable-marriage |
| Dataset Splits | No | The paper mentions using 'synthetic datasets' and 'real data' but does not specify explicit train/validation/test splits, percentages, or sample counts for these datasets. |
| Hardware Specification | Yes | The algorithms are implemented in Java4 and tested on an Intel Xeon 2.67GHz CPU with 28GB RAM. |
| Software Dependencies | No | The paper states 'The algorithms are implemented in Java4' but does not provide specific Java versions or other software dependencies with version numbers. |
| Experiment Setup | Yes | We set the probability parameter in BILS to 0, in i BILS to 0.125, the limit parameter of POWERBALANCE to n log2 2 n/10 , and the parameters in HMS to k = 2 log n and m = log n . |