Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Partitioning Friends Fairly
Authors: Lily Li, Evi Micha, Aleksandar Nikolov, Nisarg Shah
AAAI 2023 | Venue PDF | LLM Run Details | Input Tokens: 18,636 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,524 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL |
| 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. |