Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits
Authors: Etienne Boursier, Vianney Perchet
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 15,820 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,368 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic data are described in Appendix A.3. They empirically conο¬rm 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 EMAIL Vianney Perchet CMLA, ENS Paris-Saclay Criteo AI Lab, Paris EMAIL |
| 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. |