Honor Among Bandits: No-Regret Learning for Online Fair Division
Authors: Ariel D. Procaccia, Ben Schiffer, Shirley Zhang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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: arielpro@seas.harvard.edu. Department of Statistics, Harvard University | E-mail: bschiffer1@g.harvard.edu. Paulson School of Engineering and Applied Sciences, Harvard University | E-mail: szhang2@g.harvard.edu. |
| 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. |