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].
Generalized Distance Bribery
Authors: Dorothea Baumeister, Tobias Hogrebe, Lisa Rey1764-1771
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study constructive and destructive variants of distance bribery for scoring rules and obtain polynomial-time algorithms as well as NP-hardness results. We present polynomial-time algorithms obtained through dynamic programming and NP-completeness results, solving a number of open questions from the literature. |
| Researcher Affiliation | Academia | Dorothea Baumeister, Tobias Hogrebe, Lisa Rey Institut f ur Informatik Heinrich-Heine-Universit at D usseldorf 40225 D usseldorf, Germany |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found. |
| Open Source Code | No | No statement or link providing concrete access to source code for the methodology was found. |
| Open Datasets | No | The paper describes theoretical research and does not use datasets for training or evaluation, thus no information on public datasets is provided. |
| Dataset Splits | No | The paper describes theoretical research and does not involve dataset splits for training, validation, or testing, thus no specific dataset split information is provided. |
| Hardware Specification | No | The paper describes theoretical research and does not mention any specific hardware used for running experiments. |
| Software Dependencies | No | The paper describes theoretical research and does not mention specific software dependencies with version numbers needed for replication. |
| Experiment Setup | No | The paper describes theoretical research and does not involve an experimental setup with specific hyperparameters or training configurations. |