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.
Honor Among Bandits: No-Regret Learning for Online Fair Division
Authors: Ariel D. Procaccia, Ben Schiffer, Shirley Zhang
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 40,323 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,947 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 | This paper does not include any experiments. |
| Researcher Affiliation | Academia | Paulson School of Engineering and Applied Sciences, Harvard University | E-mail: EMAIL. Department of Statistics, Harvard University | E-mail: EMAIL. Paulson School of Engineering and Applied Sciences, Harvard University | E-mail: EMAIL. |
| Pseudocode | Yes | Algorithm 1 Fair Explore-Then-Commit |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. The NeurIPS checklist states: 'This paper does not include any experiments requiring code.' |
| Open Datasets | No | The paper is theoretical and does not describe experiments using a dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments using dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe experiments with specific software dependencies and version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include details on an experimental setup with specific hyperparameters or training settings. |