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 Bandits with Finite Adversarial Actions
Authors: Li Fan, Ruida Zhou, Chao Tian, Cong Shen
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiment results corroborate the theoretical analysis and demonstrate the effectiveness of Fed Sup Lin UCB on both synthetic and realworld datasets. |
| Researcher Affiliation | Academia | Li Fan University of Virginia EMAIL Ruida Zhou Texas A&M University EMAIL Chao Tian Texas A&M University EMAIL Cong Shen University of Virginia EMAIL |
| Pseudocode | Yes | Algorithm 1 Sync(s, server, client 1, . . . client n); Algorithm 2 S-LUCB; Algorithm 3 Async-Fed Sup Lin UCB; Algorithm 4 Sync-Fed Sup Lin UCB |
| Open Source Code | No | The paper does not include any explicit statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | Yes | We have carried out experiments utilizing the real-world recommendation dataset Movie Lens 20M (Harper and Konstan, 2015). |
| Dataset Splits | No | The paper mentions using datasets but does not provide specific training/validation/test split percentages, sample counts, or explicit instructions for dataset partitioning. |
| Hardware Specification | No | The paper describes experiment results using synthetic and real-world datasets but does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running these experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper mentions parameters like T, M, d, A for synthetic data and TF-IDF feature d=25, K=20 for real-world data, but does not provide specific hyperparameters (e.g., learning rate, batch size, optimizer settings) or detailed system-level training configurations. |