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 Algorithms for Kidney Exchange
Authors: Arnab Maiti, Palash Dey
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study parameterized algorithms for the kidney exchange problem in this paper. Specifically, we design FPT algorithms parameterized by each of the following parameters: (1) the number of patients who receive kidney, (2) treewidth of the input graph + max{ℓp, ℓc}, and (3) the number of vertex types in the input graph when ℓp ℓc. We also present interesting algorithmic and hardness results on the kernelization complexity of the problem. Finally, we present an approximation algorithm for an important special case of KIDNEY EXCHANGE. |
| Researcher Affiliation | Academia | Arnab Maiti1 , Palash Dey2 1,2Indian Institute of Technology Kharagpur EMAIL, EMAIL |
| Pseudocode | No | The paper describes algorithmic approaches and theoretical proofs but does not include any explicitly labeled pseudocode blocks or algorithm listings. |
| Open Source Code | No | The paper does not contain any statement about making source code for the described methodology publicly available, nor does it provide any links to a code repository. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and complexity, without describing experiments that use training data. Therefore, it does not provide access information for a public dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments or data analysis, thus no information on training, validation, or test splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments, therefore no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and complexity, without describing empirical experiments or their setup (e.g., hyperparameters, training configurations). |