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].
Parameterized Complexity of Hotelling-Downs with Party Nominees
Authors: Argyrios Deligkas, Eduard Eiben, Tiger-Lily Goldsmith
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide FPT and XP algorithms and we complement them with a W[1]-hardness result. |
| Researcher Affiliation | Academia | Argyrios Deligkas , Eduard Eiben , Tiger-Lily Goldsmith Royal Holloway, University of London EMAIL, EMAIL |
| Pseudocode | No | The paper describes algorithmic approaches, such as dynamic programming, in prose, but it does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks, nor does it present structured steps formatted like code. |
| Open Source Code | No | The paper is theoretical and does not mention providing any open-source code for the described methodologies or models. |
| Open Datasets | No | This is a theoretical paper and does not involve the use or release of any public datasets for training. |
| Dataset Splits | No | This is a theoretical paper and does not involve dataset splits for validation or any other purpose. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers for implementation or experimentation. |
| Experiment Setup | No | The paper is theoretical and does not present experimental results, therefore no experimental setup details like hyperparameters or training configurations are provided. |