Lookback Prophet Inequalities
Authors: Ziyad Benomar, Dorian Baudry, Vianney Perchet
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)? Answer: [NA] Justification: No experimental results. |
| Researcher Affiliation | Collaboration | Ziyad Benomar ENSAE, Ecole Polytechnique, Fair Play joint team ziyad.benomar@ensae.fr Dorian Baudry Department of Statistics, University of Oxford dorian.baudry@ox.ac.uk Vianney Perchet CREST, ENSAE, Criteo AI LAB Fairplay joint team vianney.perchet@normalesup.org |
| Pseudocode | No | The paper describes algorithms in prose and mathematical derivations but does not include structured pseudocode blocks or sections labeled 'Algorithm' or 'Pseudocode'. |
| Open Source Code | No | The paper is theoretical and states 'No experimental results' in its NeurIPS checklist, therefore no open-source code for the described methodology is provided or implied. |
| Open Datasets | No | The paper is theoretical and does not report experimental results using datasets. |
| Dataset Splits | No | The paper is theoretical and does not report experimental results, thus no training/test/validation dataset splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not report experimental results, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not report experimental results, thus no specific software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper is theoretical and does not report experimental results, thus no specific experimental setup details or hyperparameters are provided. |