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..
Incentivized Communication for Federated Bandits
Authors: Zhepei Wei, Chuanhao Li, Haifeng Xu, Hongning Wang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive empirical experiments on both synthetic and real-world datasets further validate the effectiveness of the proposed method across various environments. |
| Researcher Affiliation | Academia | University of Virginia1 University of Chicago2 |
| Pseudocode | Yes | Algorithm 1 INC-FEDUCB Algorithm Algorithm 2 Payment-free Incentive Mechanism Algorithm 3 Payment-efficient Incentive Mechanism Algorithm 4 Heuristic Search |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing the code or a link to a code repository. |
| Open Datasets | Yes | We also conduct comprehensive experiments on the real-world recommendation dataset Movie Lens [12]. |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits. It mentions using "synthetic and real-world datasets" but does not detail how these datasets were split for different phases of model development or evaluation. |
| Hardware Specification | No | The paper states: "We simulate the incentivized federated bandit problem under various environment settings." This implies a software simulation, and no specific hardware (like GPU/CPU models or cloud instances) is mentioned for running these simulations. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software, libraries, or programming languages used in the experiments. |
| Experiment Setup | Yes | Specifically, we create an environment of N = 50 clients with cost of data sharing Dp = {Dp 1, , Dp N}, total number of iterations T = 5, 000, feature dimension d = 25, and time-varing arm pool size K = 25. By default, we set Dp i = Dp R, i [N]. Specifically, we explored different hyper-parameter settings for INC-FEDUCB with determinant ratio threshold β [0.3, 0.7, 1], and various environmental configurations with data sharing cost Dp [1, 10, 100]. |