Thompson Sampling for Multinomial Logit Contextual Bandits
Authors: Min-hwan Oh, Garud Iyengar
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The numerical experiments show that the practical performance of both methods is in line with the theoretical guarantees. and In this section, we perform numerical evaluations to analyze two variants of our proposed algorithm: TS-MNL with optimistic sampling (Algorithm 2) and TS-MNL with the Gaussian approximation for the posterior distribution. We perform both synthetic experiments as well as simulated experiments using a real-world dataset: Movie Lens dataset. |
| Researcher Affiliation | Academia | Min-hwan Oh Columbia University New York, NY m.oh@columbia.edu Garud Iyengar Columbia University New York, NY garud@ieor.columbia.edu |
| Pseudocode | Yes | Algorithm 1 TS-MNL and Algorithm 2 TS-MNL with Optimistic Sampling |
| Open Source Code | No | The paper does not contain any statement about releasing source code or provide a link to a code repository for the methodology described. |
| Open Datasets | Yes | We perform both synthetic experiments as well as simulated experiments using a real-world dataset: Movie Lens dataset.2 ... 2https://grouplens.org/datasets/movielens/ |
| Dataset Splits | No | The paper mentions performing 'synthetic experiments' and 'simulated experiments using a real-world dataset: Movie Lens dataset' and estimates an unknown parameter using 'the entire dataset'. However, it does not explicitly provide specific training, validation, or test dataset split percentages, sample counts, or references to predefined splits within the main text. |
| Hardware Specification | No | The paper states 'The details of the experimental setup and additional experimental results are presented in Appendix G.', but no specific hardware details (like GPU/CPU models or memory amounts) are mentioned in the main body of the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies, libraries, or solvers with version numbers that would be needed for replication. |
| Experiment Setup | No | The paper mentions 'The details of the experimental setup and additional experimental results are presented in Appendix G.' but does not provide specific experimental setup details, such as concrete hyperparameter values or training configurations, in the main text. |