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..
Optimal Estimation of the Best Mean in Multi-Armed Bandits
Authors: Takayuki Osogami, Junya Honda, Junpei Komiyama
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results support our theoretical guarantees and demonstrate the practical effectiveness of our method. |
| Researcher Affiliation | Collaboration | Takayuki Osogami IBM Research Tokyo EMAIL Junya Honda Kyoto University, RIKEN AIP EMAIL Junpei Komiyama New York University, RIKEN AIP EMAIL |
| Pseudocode | Yes | Algorithm 1 Ellipsoid Est |
| Open Source Code | Yes | All the details of experimental settings are explained in Section 7 or Appendix B, and source code is submitted as the supplementary material. |
| Open Datasets | No | This paper does not use any datasets. |
| Dataset Splits | No | Our experimental settings do not involve train-test split. |
| Hardware Specification | Yes | All experiments in Appendix B, including those reported previously, were conducted on a single CPU core with 4 GB of memory and no GPU acceleration, in a cloud environment. |
| Software Dependencies | Yes | We use numpy, scipy, matplotlib, jupyterlab, which are explicitly mentioned in pyproject.toml files. |
| Experiment Setup | Yes | We choose to set the regularization parameter as λ = (R/S)2. ... We fix ε = 0.1 and study the impact of varying δ (the confidence level) and K (the number of arms). |