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..
Two-Sided Matching Meets Fair Division
Authors: Rupert Freeman, Evi Micha, Nisarg Shah
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our simulations, we observe that there is a sharp contrast for envy-freeness (one-sided EF is almost always achievable while two-sided DEF almost always isn t). For the maximin share guarantee, however, there is no contrast: both one-sided MMS and two-sided DMMS are almost always achievable. |
| Researcher Affiliation | Academia | 1University of Virginia 2University of Toronto |
| Pseudocode | Yes | Algorithm 1 Round-Robin-Ordering(n, a, x); Algorithm 2 Restricted-Round-Robin-Coprime(n, d) |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that open-source code for the described methodology is available. |
| Open Datasets | No | The paper primarily presents theoretical results and uses constructed instances (e.g., in Theorem 3 proof) rather than external, publicly available datasets. No access information for any dataset is provided. |
| Dataset Splits | No | The paper does not describe dataset splits (training, validation, test) as it focuses on theoretical analysis and proofs rather than empirical evaluation on specific datasets requiring such splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameters or system-level training settings, as its focus is theoretical and algorithm design rather than empirical model training. |