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..
Fast Asymptotically Optimal Algorithms for Non-Parametric Stochastic Bandits
Authors: Dorian Baudry, Fabien Pesquerel, Rémy Degenne, Odalric-Ambrym Maillard
NeurIPS 2023 | Venue PDF | 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 EMAIL Fabien Pesquerel Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189-CRISt AL, F-59000 Lille, France EMAIL Rémy Degenne Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189-CRISt AL, F-59000 Lille, France EMAIL Odalric-Ambrym Maillard Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189-CRISt AL, F-59000 Lille, France EMAIL |
| 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. |