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].
The Smoothed Complexity of Computing Kemeny and Slater Rankings
Authors: Lirong Xia, Weiqiang Zheng5742-5750
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we develop the ο¬rst smoothed complexity results for winner determination in voting. We prove the smoothed hardness of Kemeny and Slater using the classical smoothed runtime analysis, and prove a parameterized typical-case smoothed easiness result for Kemeny. |
| Researcher Affiliation | Academia | Lirong Xia1 and Weiqiang Zheng2 1 RPI 2 CFCS, Computer Science Dept., Peking University EMAIL, EMAIL |
| Pseudocode | Yes | ALGORITHM 1: Algorithm for EFAS. |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for its methodology is openly available. |
| Open Datasets | No | This is a theoretical paper that does not involve empirical experiments with datasets that would be publicly available for access or training. |
| Dataset Splits | No | This is a theoretical paper and does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | This is a theoretical paper and does not describe specific hardware used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not list specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not provide details about an experimental setup, hyperparameters, or training settings. |