Locally Private and Robust Multi-Armed Bandits
Authors: Xingyu Zhou, Komo(Wei) ZHANG
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Beyond our theoretical results, we have also conducted a set of simulations for our three problems. |
| Researcher Affiliation | Academia | Xingyu Zhou Wayne State University xingyu.zhou@wayne.edu Wei Zhang Texas A&M University komo@tamu.edu |
| Pseudocode | Yes | Algorithm 1 A Unified Algorithm |
| Open Source Code | Yes | We provide the code with detailed instructions for the experiments discussed in Appendix A. |
| Open Datasets | No | We consider Pareto distribution, whose probability distribution is given by f(x; xm, s) = sxs m xs+1 , if x xm 0, otherwise where s > 0 is the shape parameter and xm > 0 is the scale parameter. |
| Dataset Splits | No | The paper describes generating synthetic data based on a Pareto distribution for simulations but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | Our experiments are designed primarily to support the theoretical results and are relatively simple in their settings. They do not require high-performance hardware and can be run on most standard computers. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with version numbers for reproducibility. |
| Experiment Setup | Yes | In our experiments, we choose k = 2 and consider various corruption level α {0, 0.02, 0.05} and privacy budget ε {0.3, 0.5, 1}. For each set of parameters, we conduct 300 runs and plot the average of the estimation error and corresponding confidence region. |