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..
Communication Efficient Federated Learning for Generalized Linear Bandits
Authors: Chuanhao Li, Hongning Wang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We rigorously proved, though the setting is more general and challenging, our algorithm can attain sub-linear rate in both regret and communication cost, which is also validated by our extensive empirical evaluations. ... Extensive empirical evaluations on both synthetic and real-world datasets are performed to validate the effectiveness of our algorithm. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Virginia |
| Pseudocode | Yes | Algorithm 1 Fed GLB-UCB; Algorithm 2 ONS-Update; Algorithm 3 AGD-Update |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] |
| Open Datasets | Yes | Real-world Dataset ... Cover Type, Magic Telescope and Mushroom from the UCI Machine Learning Repository [5]. [5] Dheeru Dua and Casey Graff. UCI machine learning repository, 2017. |
| Dataset Splits | No | The paper describes parameters for the synthetic and real-world datasets (e.g., T=2000, N=200), and how data is generated/collected online in a bandit setting, but it does not specify explicit train/validation/test dataset splits with percentages or counts, which are typical for supervised learning reproducibility. |
| Hardware Specification | No | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [No] |
| Software Dependencies | No | The paper does not provide specific names and version numbers of software dependencies (e.g., programming languages, libraries, frameworks, or solvers) used for the experiments. |
| Experiment Setup | Yes | We simulated the federated GLB setting defined in Section 3.3, with T = 2000, N = 200, d = 10, S = 1, At (K = 25) uniformly sampled from a ℓ2 unit sphere, and reward yt,,i Bernoulli(µ(x t,,iθ )), with µ(z) = (1 + exp( z)) 1. ... we pre-processed these datasets following the steps in prior works [8], with T = 2000 and N = 20. ... we run Fed GLB-UCB with different threshold value D (logarithmically spaced between 10 1 and 103) and its variants with different number of global updates B. |