SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits
Authors: Etienne Boursier, Vianney Perchet
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic data are described in Appendix A.3. They empirically confirm that SICMMAB scales better than MCTop M [8] with the gaps , besides having a smaller minmax regret. |
| Researcher Affiliation | Collaboration | Etienne Boursier CMLA, ENS Paris-Saclay etienne.boursier@ens-paris-saclay.fr Vianney Perchet CMLA, ENS Paris-Saclay Criteo AI Lab, Paris vianney.perchet@normalesup.org |
| Pseudocode | Yes | The complete pseudocode of SIC-MMAB is given in Algorithm 1, Appendix A.1. Pseudocode 1: receive statistics of length p + 1. |
| Open Source Code | No | The paper does not provide any statement or link indicating that its source code is publicly available. |
| Open Datasets | No | The paper mentions "Experiments on synthetic data are described in Appendix A.3." but does not provide concrete access information or specify if this synthetic data is publicly available. |
| Dataset Splits | No | The paper mentions experiments on synthetic data but does not provide specific details about training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper describes its algorithms but does not provide specific experimental setup details, such as hyperparameter values, optimizer settings, or training configurations. |