Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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 | Input Tokens: 20,027 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,409 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |