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..
Decentralized Exploration in Multi-Armed Bandits
Authors: Raphael Feraud, Reda Alami, Romain Laroche
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To illustrate and complete the analysis of DECENTRALIZED ELIMINATION, we run three synthetic experiments: (...) The algorithms are compared with respect to two key performance indicators: the sample complexity and the communication cost. For all the experiments, ϵ is set to 0.25, and δ is set to 0.05. The privacy level η is set to 0.9. All the curves and the measures are averaged over 20 trials. |
| Researcher Affiliation | Industry | 1Orange Labs 2Microsoft Research. |
| Pseudocode | Yes | Algorithm 1 DECENTRALIZED EXPLORATION PROBLEM (...) Algorithm 2 DECENTRALIZED ELIMINATION |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release) for the source code of the described methodology. |
| Open Datasets | No | The paper uses synthetic experiments ('Problem 1: Uniform distribution of players', 'Problem 2: 50% of players generates 80% of events', 'Problem 3: non-stationary rewards') rather than external public datasets, and provides no access information or citations for any dataset. |
| Dataset Splits | No | The paper mentions running experiments over '20 trials' but does not specify any training, validation, or test dataset splits or a methodology for data partitioning. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware specifications (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using algorithms like UGAPEC and SER3, but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | For all the experiments, ϵ is set to 0.25, and δ is set to 0.05. The privacy level η is set to 0.9. |