The Secretary Problem with Predicted Additive Gap
Authors: Alexander Braun, Sherry Sarkar
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Complementing theoretical results, we run simulations in Section 6 which strengthen our theoretical findings. |
| Researcher Affiliation | Academia | Institute of Computer Science, University of Bonn. Email: alexander.braun@uni-bonn.de Carnegie Mellon University. Email: sherrys@andrew.cmu.edu |
| Pseudocode | Yes | Algorithm 1 Secretary with Exact Additive Gap |
| Open Source Code | No | We will make the code and data publicly available after the review process. |
| Open Datasets | No | The paper describes generating data by drawing weights i.i.d. from distributions (Pareto, Exponential, Chi-Squared) but does not provide concrete access information (link, DOI, citation) to a pre-existing, publicly available dataset. |
| Dataset Splits | No | The paper describes generating data for simulations (drawing weights i.i.d.) and does not mention explicit validation, training, or test splits of a pre-existing dataset. The NeurIPS checklist also states 'NA' for this question. |
| Hardware Specification | Yes | All experiments were implemented in Python 3.9 and executed on a machine with Apple M1 and 8 GB Memory. |
| Software Dependencies | Yes | All experiments were implemented in Python 3.9 and executed on a machine with Apple M1 and 8 GB Memory. |
| Experiment Setup | Yes | For each class of instances, we average over 5000 iterations. In each iteration, we draw n = 200 weights i.i.d. from the respective distribution together with 200 arrival times which are drawn i.i.d. from Unif[0, 1]. |