Dynamic Learning in Large Matching Markets

Authors: Anand Kalvit, Assaf Zeevi

NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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 {1akalvit22,2assaf}@gsb.columbia.edu
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.