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].
Comparing Approximate Relaxations of Envy-Freeness
Authors: Georgios Amanatidis, Georgios Birmpas, Vangelis Markakis
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We establish several tight, or almost tight, results concerning the approximation quality that any of these notions guarantees for the others, providing an almost complete picture of this landscape. Proof. We prove the statement for n = 2. |
| Researcher Affiliation | Academia | 1 Centrum Wiskunde & Informatica (CWI), Amsterdam, the Netherlands 2 Department of Informatics, Athens University of Economics and Business, Athens, Greece |
| Pseudocode | No | The paper describes algorithms verbally (e.g., 'round-robin algorithm') but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper uses theoretical examples (e.g., instance with 3 agents and 5 items in Example 1) but does not utilize or refer to any publicly available or open datasets for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation with data splits (training, validation, or test). |
| Hardware Specification | No | The paper is theoretical and does not report on experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers required to replicate any experiments or computational processes related to its contributions. |
| Experiment Setup | No | The paper is theoretical and does not include details on experimental setup, hyperparameters, or training configurations. |