Federated Linear Contextual Bandits
Authors: Ruiquan Huang, Weiqiang Wu, Jing Yang, Cong Shen
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate the effectiveness of the proposed algorithms on both synthetic and real-world datasets. |
| Researcher Affiliation | Collaboration | Ruiquan Huang The Pennsylvania State University rzh5514@psu.edu Weiqiang Wu Facebook weiqiang.wwu@gmail.com Jing Yang The Pennsylvania State University yangjing@psu.edu Cong Shen University of Virginia cong@virginia.edu |
| Pseudocode | Yes | Algorithm 1 Fed-PE : client i |
| Open Source Code | No | The paper does not provide an explicit statement about releasing code or a link to a code repository. |
| Open Datasets | Yes | Movielens Dataset: We then use the Movie Lens-100K dataset (Harper and Konstan, 2015) to evaluate the performances. |
| Dataset Splits | No | The paper mentions using synthetic and Movie Lens datasets, but does not provide specific training/validation/test splits, percentages, or predefined split citations. |
| Hardware Specification | No | No specific hardware details (GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running experiments are provided. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned in the paper. |
| Experiment Setup | Yes | For all experiments, we set T = 2^17, f p = 2p, p {1, 2, . . . , 16}, and run 10 trials. For Fed-PE and its variants, we choose δ = 0.1. |