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..
ODSS: Efficient Hybridization for Optimal Coalition Structure Generation
Authors: Narayan Changder, Samir Aknine, Sarvapali Ramchurn, Animesh Dutta7079-7086
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | When compared to the state-of-the-art against a wide variety of value distributions, ODSS is shown to perform better by up to 54.15% on benchmark inputs. |
| Researcher Affiliation | Academia | 1National Institute of Technology Durgapur, India. 2LIRIS, Lyon 1 University, France. 3University of Southampton,UK. |
| Pseudocode | Yes | Algorithm 1 Subspace division technique; Algorithm 2 ODSS Algorithm |
| Open Source Code | No | The paper states that ODP-IP and ODSS were implemented in Java, and that they used code provided by the authors of ODP-IP. However, there is no explicit statement or link indicating that the code for ODSS is publicly available or open-source. |
| Open Datasets | No | The paper describes various data generation distributions (e.g., Agent-based Uniform, Chi-square) used for evaluation, but it does not refer to or provide access information for a specific publicly available or open dataset. |
| Dataset Splits | No | The paper describes running experiments with averages over 50 tests/runs for each distribution, but it does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | Yes | Both ODP-IP and ODSS were implemented in Java, and the experiments were run on an Intel(R) Xeon(R) CPU E7-4830 v3 with 160 GB of RAM. |
| Software Dependencies | No | The paper states that the implementation was done 'in Java' but does not provide specific version numbers for Java or any other software dependencies. |
| Experiment Setup | No | The paper describes the data generation distributions used for experiments but does not provide specific hyperparameters or system-level training settings for the algorithms, such as learning rates, batch sizes, or optimizer configurations. |