Bandits Dueling on Partially Ordered Sets
Authors: Julien Audiffren, Liva Ralaivola
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we report results on the empirical performance of our algorithm in different settings (Section 5). Section 5.1 Simulated Poset. 5.2 Movie Lens Dataset. |
| Researcher Affiliation | Academia | Julien Audiffren CMLA ENS Paris-Saclay, CNRS Universit e Paris-Saclay, France julien.audiffren@gmail.com Liva Ralaivola Lab. Informatique Fondamentale de Marseille CNRS, Aix Marseille University Institut Universitaire de France F-13288 Marseille Cedex 9, France liva.ralaivola@lif.univ-mrs.fr |
| Pseudocode | Yes | Algorithm 1 Direct comparison; Algorithm 2 Unchained Bandits; Algorithm 3 UBSRoutine; Algorithm 4 Decoy comparison. |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that the code for the described methodology is publicly available. |
| Open Datasets | Yes | Movie Lens dataset (Harper and Konstan [2015]) |
| Dataset Splits | No | The paper describes how the simulated posets are generated and how the Movie Lens dataset is preprocessed (e.g., "remove all films with less than 50000 evaluations"), but it does not specify explicit training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper mentions numerical simulations but does not specify any hardware details like GPU/CPU models, memory, or specific computing environments used for the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python version, library versions) used for its implementation or experiments. |
| Experiment Setup | Yes | By default, we use δ = 1/1000 and = 1/100, β = 0.9 and N = blog(K)/ log β)c. |