Best Arm Identification for Stochastic Rising Bandits
Authors: Marco Mussi, Alessandro Montenegro, Francesco Trovò, Marcello Restelli, Alberto Maria Metelli
ICML 2024 | Conference PDF | Archive PDF | Plain Text | 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. |