Fast Asymptotically Optimal Algorithms for Non-Parametric Stochastic Bandits
Authors: Dorian Baudry, Fabien Pesquerel, Rémy Degenne, Odalric-Ambrym Maillard
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, in Section 4 we perform numerical simulations that confirm the benefits of our novel algorithms in terms of computation time, and show their strong empirical performance. |
| Researcher Affiliation | Academia | Dorian Baudry Ecole Polytechnique, CREST Palaiseau, France dorian.baudry@ensae.fr Fabien Pesquerel Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189-CRISt AL, F-59000 Lille, France fabien.pesquerel@inria.fr Rémy Degenne Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189-CRISt AL, F-59000 Lille, France remy.degenne@inria.fr Odalric-Ambrym Maillard Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189-CRISt AL, F-59000 Lille, France odalric.maillard@inria.fr |
| Pseudocode | Yes | We provide a condensed implementation of OIMED in Algorithm 1, and the detailed implementation of OMED in Appendix A.2 (Algorithm 6). |
| Open Source Code | Yes | Our code is available in the supplementary material of the paper. |
| Open Datasets | Yes | The dataset is available in the supplementary material of the paper. |
| Dataset Splits | No | The paper discusses experimental evaluations but does not explicitly provide details on training, validation, or test dataset splits, percentages, or cross-validation setups. |
| Hardware Specification | No | The paper mentions 'Python implementation' for run times but does not specify any particular hardware components (e.g., CPU, GPU models, memory) used for the experiments. |
| Software Dependencies | No | The paper mentions a 'Python implementation' and refers to 'Soft-Bayes' as a portfolio selection algorithm, but it does not provide specific version numbers for Python, Soft-Bayes, or any other software libraries. |
| Experiment Setup | Yes | We illustrate the stability of OIMED on three bandit settings: the DSSAT bandit problem and Bernoulli problem that were introduced in the main Section 4 and a Beta bandit problem where all the means are centered around 0.5 and the same as in the Bernoulli bandit. ... We will replace this original η by ηn = r q / 4n where r will range from 0.01 to 100. |