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..
Federated Combinatorial Multi-Agent Multi-Armed Bandits
Authors: Fares Fourati, Mohamed-Slim Alouini, Vaneet Aggarwal
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate our approach to a stochastic data summarization problem, illustrating the effectiveness of the proposed framework, even in single-agent scenarios. |
| Researcher Affiliation | Academia | 1Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, KSA. 2School of Industrial Engineering, Purdue University, West Lafayette, IN 47907, USA. Correspondence to: Fares Fourati <EMAIL>. |
| Pseudocode | Yes | We present our proposed C-MA-MAB Framework; see Algorithm 1. |
| Open Source Code | No | The paper does not provide any statement or link for open-source code for the methodology. |
| Open Datasets | Yes | We run experiments on FMNIST (Xiao et al., 2017) and CIFAR10 (Krizhevsky et al., 2009), present the latter in the main paper, and relegate more details and results to Appendix F. |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset splits. It mentions running experiments on FMNIST and CIFAR10, but no details on how these datasets were partitioned for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details with version numbers. |
| Experiment Setup | Yes | We set a cardinality constraint of k = 5. Our goal is to summarize information from fifteen images, and instead of comparing it to all the images, we only consider a random batch R of 3 images. We run the experiments 100 times. We test for several time horizons in {125, 250, 500, 1000, 2000, 4000, 8000, 12000, 16000, 20000}. |