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].
Deterministic, Strategyproof, and Fair Cake Cutting
Authors: Vijay Menon, Kate Larson
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | All our negative (impossibility) results are in the direct-revelation model, while the algorithm we present is in the Robertson Webb model. |
| Researcher Affiliation | Academia | Vijay Menon and Kate Larson David R. Cheriton School of Computer Science University of Waterloo EMAIL |
| Pseudocode | Yes | Algorithm 1: ϵ-approximation of a piecewise constant function using 2k/ϵ cut queries. Algorithm 2: Modified Even-Paz algorithm. |
| Open Source Code | No | The paper does not contain any explicit statement about making its source code publicly available, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and focuses on mathematical proofs and algorithm analysis; it does not report on empirical experiments that would involve training on a dataset. Therefore, no information about publicly available training datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets; thus, no information about training, validation, or test dataset splits is provided. |
| Hardware Specification | No | The paper is theoretical and focuses on mathematical proofs and algorithm analysis; it does not describe any empirical experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical proofs and algorithm analysis; it does not describe specific software implementations with version numbers that would be required for replication. Therefore, no software dependencies are listed. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments. Thus, no experimental setup details, hyperparameters, or training configurations are provided. |