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..
Dynamic Learning in Large Matching Markets
Authors: Anand Kalvit, Assaf Zeevi
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments showing O (log n) achievable regret are provided in the appendix. |
| Researcher Affiliation | Academia | Anand Kalvit1 and Assaf Zeevi2 Columbia University, New York EMAIL |
| Pseudocode | Yes | Algorithm 1 MATCH, Algorithm 2 CAB-K |
| Open Source Code | No | The authors stated '[N/A]' for the question 'Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)?' in the checklist. There is no other explicit statement or link indicating that the source code for the methodology is provided. |
| Open Datasets | No | The paper states '[N/A]' for questions related to including 'data' in the checklist. While it mentions applications like 'Amazon Mechanical Turk', it does not specify any publicly available datasets used for its numerical experiments. |
| Dataset Splits | No | The paper does not provide any specific information about training, validation, or test dataset splits. The checklist also indicates '[N/A]' for experimental details. |
| Hardware Specification | No | The checklist for the paper states '[N/A]' for the question regarding the inclusion of compute resources and hardware specifications. The main text does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper does not provide a reproducible description of ancillary software with specific version numbers. The checklist indicates '[N/A]' for training details which would typically include software dependencies. |
| Experiment Setup | No | The paper does not explicitly provide details about the experimental setup such as hyperparameters or system-level training settings. The checklist states '[N/A]' for specifying training details. |