Partitioning Friends Fairly
Authors: Lily Li, Evi Micha, Aleksandar Nikolov, Nisarg Shah
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide (often tight) approximations to both fairness guarantees, and many of our positive results are obtained via efficient algorithms. |
| Researcher Affiliation | Academia | Department of Computer Science, University of Toronto {xinyuan, emicha, anikolov, nisarg}@cs.toronto.edu |
| Pseudocode | Yes | Algorithm 1: Local Min-Cut |
| Open Source Code | No | The paper focuses on theoretical results, algorithms, and proofs. It does not mention any implementation of the described algorithms or the release of corresponding source code. |
| Open Datasets | No | The paper uses theoretical graph structures (e.g., K3,3,3 graph, cycle graphs) to illustrate concepts and proofs. It does not mention or provide access information for any real-world public datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments on datasets; therefore, it does not provide dataset split information for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments. Therefore, there is no mention of hardware specifications used for running experiments. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithms and proofs. It does not mention any specific software dependencies or version numbers required for implementation or replication. |
| Experiment Setup | No | The paper is theoretical and does not report on empirical experiments. Therefore, it does not provide details on experimental setup, such as hyperparameters or training configurations. |