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..
Thompson Sampling via Local Uncertainty
Authors: Zhendong Wang, Mingyuan Zhou
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results on eight contextual bandit benchmark datasets show that Thompson sampling guided by local uncertainty achieves state-of-the-art performance while having low computational complexity. |
| Researcher Affiliation | Academia | 1Mc Combs School of Business, The University of Texas at Austin, Austin, TX 78712, USA. Correspondence to: Mingyuan Zhou <EMAIL>. |
| Pseudocode | Yes | We describe two different versions of TS via LU, as will be discussed in detail, in Algorithms 2 and 3 in the Appendix, respectively. |
| Open Source Code | Yes | Python (Tensor Flow 1.14) code for both LU-Gauss and LU-SIVI is available at https://github.com/Zhendong-Wang/Thompson-Sampling-via-Local-Uncertainty |
| Open Datasets | Yes | We consider eight different datasets from this benchmark, including Mushroom, Financial, Statlog, Jester, Wheel, Covertype, Adult, and Census, which exhibit a wide variety of statistical properties. Details on these datasets are provided in Table 3. |
| Dataset Splits | No | The paper describes a sequential learning setup for contextual bandits where the agent continuously interacts with the environment and updates its estimates. It does not provide traditional train/validation/test splits with percentages or sample counts common in supervised learning. |
| Hardware Specification | Yes | We report the time cost based on an Nvidia 1080-TI GPU. |
| Software Dependencies | Yes | Python (Tensor Flow 1.14) code for both LU-Gauss and LU-SIVI is available at https://github.com/Zhendong-Wang/Thompson-Sampling-via-Local-Uncertainty |
| Experiment Setup | Yes | For both LU-Gauss and LU-SIVI, we choose the Adam optimizer with the learning rate set as 10 3. |