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..
Resource Sharing through Multi-Round Matchings
Authors: Yohai Trabelsi, Abhijin Adiga, Sarit Kraus, S. S. Ravi, Daniel J. Rosenkrantz
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally evaluate our algorithms on synthetic networks and apply them to two real-world situations: shared of๏ฌce spaces and matching courses to classrooms. |
| Researcher Affiliation | Academia | Yohai Trabelsi1, Abhijin Adiga2, Sarit Kraus1, S. S. Ravi2, 3, Daniel J. Rosenkrantz2, 3 1 Department of Computer Science, Bar-Ilan University, Ramat Gan, Israel 2 Biocomplexity Institute and Initiative, Univ. of Virginia, Charlottesville, VA, USA 3 Dept. of Computer Science, University at Albany SUNY, Albany, NY, USA |
| Pseudocode | Yes | Algorithm 1: ALG-MAXTB-MRM |
| Open Source Code | Yes | The data and the code for running the experiments are available at https://github.com/yohayt/Resource-Sharing-Through-Multi-Round-Matchings. |
| Open Datasets | Yes | The data and the code for running the experiments are available at https://github.com/yohayt/Resource-Sharing-Through-Multi-Round-Matchings. |
| Dataset Splits | No | The paper mentions data generation and replication but does not specify explicit train/validation/test dataset splits for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions solution approaches like an Integer Linear Program (ILP) but does not provide specific version numbers for software dependencies or solvers. |
| Experiment Setup | No | The paper describes the general experimental settings and scenarios (e.g., number of rounds, preference thresholds) but does not provide specific hyperparameters or detailed system-level training configurations. |