Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Matching with Dynamic Ordinal Preferences
Authors: Hadi Hosseini, Kate Larson, Robin Cohen
AAAI 2015 | Venue PDF | LLM Run Details | Input Tokens: 17,700 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,521 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We formulate a generic dynamic matching problem via a sequential stochastic matching process. We design a mechanism based on random serial dictatorship (RSD) that, given any history of preferences and matching decisions, guarantees global stochastic strategyproofness while satisfying desirable local properties. |
| Researcher Affiliation | Academia | Hadi Hosseini Cheriton School of Computer Science University of Waterloo Waterloo, ON, Canada EMAIL Kate Larson Cheriton School of Computer Science University of Waterloo Waterloo, ON, Canada EMAIL Robin Cohen Cheriton School of Computer Science University of Waterloo Waterloo, ON, Canada EMAIL |
| Pseudocode | Yes | Algorithm 1: RSD with adjusted priorities (ARSD) |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit release statement) for open-source code. |
| Open Datasets | No | The paper is theoretical and does not involve the use of datasets for training. Therefore, no information about publicly available datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve the use of datasets with training, validation, or test splits. Therefore, no such information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware. No hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any software implementation with specific version numbers for dependencies. |
| Experiment Setup | No | The paper is theoretical and describes algorithm design and proofs, not an experimental setup with hyperparameters or system-level training settings. |