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 Linear Contextual Bandits
Authors: Ruiquan Huang, Weiqiang Wu, Jing Yang, Cong Shen
NeurIPS 2021 | Venue PDF | 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 EMAIL Weiqiang Wu Facebook EMAIL Jing Yang The Pennsylvania State University EMAIL Cong Shen University of Virginia EMAIL |
| 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. |