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..
On Differentially Private Federated Linear Contextual Bandits
Authors: Xingyu Zhou, Sayak Ray Chowdhury
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we support our theoretical results with numerical evaluations over contextual bandit instances generated from both synthetic and real-life data. and 6 SIMULATION RESULTS AND CONCLUSIONS We evaluate regret performance of Algorithm 1 under silo-level LDP and SDP, which we abbreviate as LDP-Fed Lin UCB and SDP-Fed Lin UCB, respectively. |
| Researcher Affiliation | Collaboration | Xingyu Zhou Wayne State University, USA Email: EMAIL Sayak Ray Chowdhury Microsoft Research, India Email: EMAIL |
| Pseudocode | Yes | Algorithm 1 Private-Fed Lin UCB |
| Open Source Code | No | The paper does not explicitly state that the code for the described methodology is open-source or provide a link to a code repository. |
| Open Datasets | Yes | We generate bandit instances from Microsoft Learning to Rank dataset (Qin & Liu, 2013). |
| Dataset Splits | No | The paper discusses the use of synthetic and real-life data for evaluation but does not specify explicit training/validation/test dataset splits (e.g., percentages, sample counts, or predefined splits). |
| Hardware Specification | No | The paper does not specify any particular hardware components (e.g., GPU/CPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks) used for implementation or experimentation. |
| Experiment Setup | Yes | We fix confidence level α=0.01, batchsize B = 25 and study comparative performances under varying privacy budgets ε, δ. |