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..
Problem Dependent View on Structured Thresholding Bandit Problems
Authors: James Cheshire, Pierre Menard, Alexandra Carpentier
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Appendix ?? we conduct some preliminary experiments to explore how our theoretical results translate in practice. All proofs are found in the Appendix. |
| Researcher Affiliation | Academia | 1Otto von Guericke University Magdeburg. Correspondence to: James Cheschire <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 PD-MTB; Algorithm 2 Grad-Explore; Algorithm 3 PD-CTB |
| Open Source Code | No | The paper does not provide any explicit statements about the release of source code or links to a code repository. |
| Open Datasets | No | The paper focuses on a theoretical bandit problem setting and describes general problem formulations (e.g., 'K-armed bandit problem', 'unknown distribution νk'). It mentions 'preliminary experiments' but provides no specific details, names, links, or citations for any publicly available datasets used. |
| Dataset Splits | No | The paper does not specify any training, validation, or test dataset splits. The research is primarily theoretical with preliminary experiments mentioned but not detailed in terms of data splits. |
| Hardware Specification | No | The paper mentions 'preliminary experiments' but does not provide any specific details about the hardware used to conduct these experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions 'preliminary experiments' but does not provide specific version numbers for any software dependencies (e.g., programming languages, libraries, frameworks) used in these experiments. |
| Experiment Setup | No | The paper describes the theoretical setup of the Thresholding Bandit Problem and the parameters of its proposed algorithms (e.g., T1 and T2 derived from T and K). However, it does not provide specific experimental setup details such as hyperparameters, optimization settings, or system-level training configurations that would be used in an empirical implementation of the algorithms. |