Shuffle Private Linear Contextual Bandits
Authors: Sayak Ray Chowdhury, Xingyu Zhou
ICML 2022 | Conference PDF | Archive PDF | Plain Text | 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 <sayak@bu.edu>, Xingyu Zhou <xingyu.zhou@wayne.edu>. |
| 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. |