The Dichotomous Affiliate Stable Matching Problem: Approval-Based Matching with Applicant-Employer Relations
Authors: Marina Knittel, Samuel Dooley, John Dickerson
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | we use a human study to show that realworld matching rankings follow our assumed valuation function; (2) we prove that there always exists a stable solution by providing an efficient, easilyimplementable algorithm that finds such a solution; and (3) we experimentally validate the efficiency of our algorithm versus a linear-programming-based approach. |
| Researcher Affiliation | Academia | Marina Knittel , Samuel Dooley , John P. Dickerson Computer Science Department, University of Maryland {mknittel, sdooley1, john}@cs.umd.edu |
| Pseudocode | Yes | All proofs and pseudocode can be found in the full version of this paper. |
| Open Source Code | No | The paper states 'All proofs and pseudocode can be found in the full version of this paper,' but does not provide an unambiguous statement of open-source code release or a direct link to a repository for the described methodology. |
| Open Datasets | Yes | We use similar runtime experiments used by Tziavelis et al. [2019] adapted to the DASM setting. ... We use Tziavelis et al. [2019] s Uniform data... |
| Dataset Splits | No | The paper describes how synthetic data is generated for each trial based on parameters like 'uniform random total ranking' and 'threshold parameter,' but it does not specify explicit 'training/test/validation dataset splits' as typically understood for machine learning experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions solving the ILP using 'state-of-the-art ILP solvers such as Gurobi' but does not specify exact software names with version numbers for reproducibility. |
| Experiment Setup | Yes | We have four parameters: (1) m, the number of employers, (2) n/m, the number of affiliates per employer, (3) q, the capacity for each applicant, and (4) t (0, 1), a threshold parameter. ... Fix n m = 2, q = 3, and t = 0.5. |