Tsetlin Machine for Solving Contextual Bandit Problems
Authors: Raihan Seraj, Jivitesh Sharma, Ole-Christoffer Granmo
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical analysis shows that Tsetlin Machine as a base contextual bandit learner outperforms other popular comparable base learners on eight out of nine datasets. |
| Researcher Affiliation | Academia | Raihan Seraj Electrical and Computer Engineering Mc Gill University Canada raihan.seraj@mail.mcgill.ca Jivitesh Sharma Center for Artificial Intelligence Research (CAIR) University of Agder Norway jivitesh.sharma@uia.no Ole-Christoffer Granmo Center for Artificial Intelligence Research (CAIR) University of Agder Norway ole.granmo@uia.no |
| Pseudocode | Yes | Algorithm 1 Complete WTM learning process; Algorithm 2 Thompson Sampling with TM |
| Open Source Code | Yes | The code is available online on: github |
| Open Datasets | Yes | We use standard supervised learning classification datasets from UCI machine learning repository [10]: Iris, Breast Cancer Wisconsin (Diagnostic), Adult, Covertype, and Statlog (Shuttle). ... Additionally we considered two other classification datasets: MNIST [9] and Noisy XOR... Movielens 100 K [15] and Simulated Article [23]. |
| Dataset Splits | No | The paper states that training details including data splits are provided in Appendix B, but Appendix B is not included in the provided text. The main body of the paper does not explicitly detail the specific training, validation, or test data splits used for the experiments. |
| Hardware Specification | No | The paper explicitly states in its self-assessment checklist (3d) that it did not include the total amount of compute and the type of resources used for the experiments. |
| Software Dependencies | No | The paper does not provide a reproducible description of ancillary software, specifically lacking version numbers for key software components or libraries used for the experiments. |
| Experiment Setup | Yes | Details of binarization and parameter choices are provided in Appendix B. |