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].
Query Complexity of Tournament Solutions
Authors: Palash Dey
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we prove tight bounds on the query complexity of commonly used tournament solutions. |
| Researcher Affiliation | Academia | Palash Dey Indian Institute of Science, Bangalore |
| Pseudocode | No | The paper describes algorithms in prose and mathematical notation (e.g., Theorem 6 and 7), but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper describes theoretical work and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper describes theoretical work and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes theoretical work and does not provide details on an experimental setup, hyperparameters, or training configurations. |