Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Fully Online Matching with Stochastic Arrivals and Departures
Authors: Zihao Li, Hao Wang, Zhenzhen Yan
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive numerical studies to evaluate the performance of our algorithms. |
| Researcher Affiliation | Academia | Zihao Li, Hao Wang, Zhenzhen Yan Nanyang Technological University EMAIL, hao EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: SAM(γ) Input: Online arrivals of agents Parameter: Scaling parameter γ (0, 1] |
| Open Source Code | No | The paper does not provide a direct link to open-source code for the described methodology or state that the code will be made publicly available. It only mentions using Gurobi as a solver. |
| Open Datasets | No | The paper describes generating synthetic datasets for experiments but does not provide access information (link, citation, or repository) for these generated datasets to be publicly available. |
| Dataset Splits | No | The paper discusses evaluating performance over "realized sequences" and states "For each parameter setting, we test |R| = 50 realized sequences." However, it does not specify traditional train/validation/test dataset splits, which are not directly applicable to its online problem setup. |
| Hardware Specification | Yes | We use a computer with 2.2 GHz Intel Core i7 processor, 16 GB 1600 MHz DDR3 memory and Intel Iris Pro 1536 MB Graphics to run all the experiments. |
| Software Dependencies | No | The paper mentions "We use Gurobi (Gurobi Optimization 2022) as our solver." While Gurobi is a specific software, it provides the year of the reference (2022) but not a numerical version number (e.g., Gurobi 9.5). |
| Experiment Setup | Yes | The parameters are q = 2.5 and geometric distribution with P G = 0.5. We set T = 3000 which is much larger than the sojourn time of any vertex under any tested distribution. |