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..
Shuffle Private Linear Contextual Bandits
Authors: Sayak Ray Chowdhury, Xingyu Zhou
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We further perform simulations on synthetic data that corroborate our theoretical results. In this section, we empirically evaluate the regret performance of Algorithm 1 (under shuffle model), which we abbreviate as Lin UCB-SDP-Amp and Lin UCB-SDP-Vec when instantiated with PAmp and PVec, respectively. |
| Researcher Affiliation | Academia | 1Boston University, USA 2Wayne State University, USA. Correspondence to: Sayak Ray Chowdhury <EMAIL>, Xingyu Zhou <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Shuffle Private Lin UCB |
| Open Source Code | Yes | Code is available at https://github.com/sayakrc/Differentially-Private-Bandits. |
| Open Datasets | No | We further perform simulations on synthetic data that corroborate our theoretical results. The paper mentions "synthetic data" but does not provide any access information or citation for a public dataset. |
| Dataset Splits | No | For all the experiments, we consider 100 arms, set T = 20000 rounds, and average our results over 50 randomly generated bandit instances. Each instance is characterized by an (unknown) parameter θ and feature vectors of dimension d = 5. The paper describes the simulation parameters but does not specify train/validation/test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models or types of machines used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | For all the experiments, we consider 100 arms, set T = 20000 rounds, and average our results over 50 randomly generated bandit instances. Each instance is characterized by an (unknown) parameter θ and feature vectors of dimension d = 5. ... We fix δ =0.1 and plot the results for varying privacy level ε {0.2, 1, 10}. We use Batchsize B = 20 for Lin UCB-SDP. |