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 [1].
Refining Tournament Solutions via Margin of Victory
Authors: Markus Brill, Ulrike Schmidt-Kraepelin, Warut Suksompong1862-1869
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the computational complexity of the Mo V with respect to several common tournament solutions, including the Copeland set, the top cycle, the uncovered set, and the Banks set. For each tournament solution, we determine the complexity of computing the Mo V for both winners and nonwinners, in both the unweighted and weighted setting. In addition, we derive tight or asymptotically tight lower and upper bounds on the Mo V for all of the considered tournament solutions in the unweighted setting. |
| Researcher Affiliation | Academia | Markus Brill Technische Universit at Berlin Chair of Efficient Algorithms EMAIL; Ulrike Schmidt-Kraepelin Technische Universit at Berlin Chair of Efficient Algorithms EMAIL; Warut Suksompong University of Oxford Department of Computer Science EMAIL |
| Pseudocode | No | The paper describes algorithms and proofs in text but does not include explicit pseudocode blocks or labeled algorithm sections. |
| Open Source Code | No | The paper does not provide any statements about open-sourcing code for its methodology or links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not describe empirical experiments that involve training on a dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments that involve a validation set. |
| Hardware Specification | No | The paper does not describe any specific hardware used for running experiments, as it is a theoretical paper. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers, as it is a theoretical paper and does not describe empirical implementations requiring such details. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |