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..
Secretary Matching with Vertex Arrivals and No Rejections
Authors: Mohak Goyal5051-5058
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | All our algorithms run in polynomial time. The competitive analysis results hold in expectation, which is taken over the randomness in the arrival order and in the algorithm. |
| Researcher Affiliation | Academia | Department of Management Science & Engineering, Stanford University EMAIL |
| Pseudocode | Yes | Algorithm 1: ALG1 for BIPARTITEMATCHING1; Algorithm 2: ALG2 for BIPARTITEMATCHING2; Algorithm 3: ALG3 for GENERALMATCHING; Algorithm 4: ALG4 for ROOMMATEMATCHING |
| Open Source Code | No | The paper mentions an 'arxiv preprint arxiv:2112.07140' but does not state that source code for the described methods is openly available. |
| Open Datasets | No | The paper operates on theoretical graph models with arbitrary non-negative edge-weights and does not describe experiments using specific datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets, thus no dataset splits for validation are mentioned. |
| Hardware Specification | No | The paper focuses on theoretical algorithms and their competitive analysis, not empirical experiments. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe an implementation, hence no specific software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper describes algorithmic phases and parameters (e.g., stopping points k, ke, ks) inherent to the algorithms themselves, but these are not experimental setup details like hyperparameters for a machine learning model training. |