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..
Constrained Market Share Maximization by Signal-Guided Optimization
Authors: Bo Hui, Yuchen Fang, Tian Xia, Sarp Aykent, Wei-Shinn Ku
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiment, we empirically verify the superiority of both our prediction model and optimization approach over existing works with large-scale real-world data. |
| Researcher Affiliation | Academia | 1 Auburn University 2 Beijing University of Posts and Telecommunications |
| Pseudocode | Yes | Algorithm 1: Signal-guided two-timescale Optimization |
| Open Source Code | Yes | Our code has been released at: https://github.com/coding And BS/Airline Market. |
| Open Datasets | Yes | We conduct extensive experiments on the airline origin and destination survey (DB1B) dataset1 released by the US Department of Transportation s Bureau of Transportation Statistics (BTS), which records the flight route information in the US and releases 10% of tickets sold in the US every month of the year for research purposes. 1https://www.transtats.bts.gov/Data Index.asp |
| Dataset Splits | Yes | From the prediction perspective, we aim to learn a neural network-based model by leveraging data of the top 3 airlines in the past ten years except for the last month s data. The data of the last month is used for testing. ... To prevent over-fitting for the data of last month, we use the 10-fold cross-validation method to choose the best hyper-parameter, which means we have nine 12-month folds and one 11-month fold. Following the standard cross-validation, we randomly select nine folds to train our model and utilize the remaining fold as the validation set to tune hyper-parameters and repeat this process nine times. |
| Hardware Specification | No | The paper does not specify any particular hardware details such as GPU models, CPU models, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of 'Adam optimizer' but does not specify version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | We train our market share prediction model with the learning rate of 1e-3, epochs of 500, and the Adam optimizer for updating weights. The number of structure-aware attention layers in our model is 4 and the dimension of the hidden states in our model is 64. |