Learning Mixed Multinomial Logits with Provable Guarantees
Authors: Yiqun Hu, David Simchi-Levi, Zhenzhen Yan
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct simulation studies to evaluate the performance and demonstrate how to apply our algorithm to real-world applications. |
| Researcher Affiliation | Academia | Yiqun Hu MIT huyiqun@mit.edu David Simchi-Levi MIT dslevi@mit.edu Zhenzhen Yan Nanyang Technological University yanzz@ntu.edu.sg |
| Pseudocode | Yes | Algorithm 1: Stochastic Subregion Frank-Wolfe |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] See Appendix B. The code for generating synthetic data can be found on our Github repo: https://github.com/yiqunhu/SSRFW. |
| Open Datasets | No | We apply our SSRFW algorithm to a dataset of customer choices from a large online retailer... The dataset consists of customer choices of mobile phone brands for 21 weeks between November 2021 and March 2022. |
| Dataset Splits | No | We split the data into 16 weeks for training and the last 5 weeks for testing. |
| Hardware Specification | No | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A] |
| Software Dependencies | No | The code for generating synthetic data can be found on our Github repo: https://github.com/yiqunhu/SSRFW. |
| Experiment Setup | Yes | The number of subsamples for Q-construction is set to L = 500, and n = 100 as the subsample size. The number of iterations for SSRFW is set to 200. We calibrate all other parameters to be consistent with our synthetic data experiments, i.e., M = 5, D = 2. |