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..
Best Arm Identification for Stochastic Rising Bandits
Authors: Marco Mussi, Alessandro Montenegro, Francesco Trovò, Marcello Restelli, Alberto Maria Metelli
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we numerically validate the proposed algorithms in both synthetic and realistic environments. |
| Researcher Affiliation | Academia | 1Politecnico di Milano, Milan, Italy. |
| Pseudocode | Yes | Algorithm 1: R-UCBE. Algorithm 2: R-SR. |
| Open Source Code | Yes | The code to reproduce the experiments can be found at https://github.com/MontenegroAlessandro/BestArmIdSRB. |
| Open Datasets | No | The paper describes using a 'synthetic Gaussian SRB' and the 'IMDB dataset', but does not provide concrete access information (link, DOI, repository, or formal citation with authors/year) for the IMDB dataset. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits (e.g., percentages, sample counts, or explicit mention of standard splits with citations). |
| Hardware Specification | Yes | The code used for the results provided in this section has been run on an Intel(R) I7 9750H @ 2.6GHz CPU with 16 GB of LPDDR4 system memory. The operating system was Mac OS 13.1, and the experiments were run on Python 3.10. |
| Software Dependencies | No | The paper mentions 'Python 3.10' as the execution environment but does not provide specific version numbers for other key software libraries or dependencies used in the experiments. |
| Experiment Setup | Yes | For both our algorithms and RR-SW, we set ε 0.25. |