On Private and Robust Bandits
Authors: Yulian Wu, Xingyu Zhou, Youming Tao, Di Wang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we support our theoretical results and show the effectiveness of our algorithms with experimental studies. |
| Researcher Affiliation | Academia | KAUST yulian.wu@kaust.edu.sa Xingyu Zhou Wayne State University xingyu.zhou@wayne.edu Youming Tao Shandong University ym.tao99@mail.sdu.edu.cn KAUST di.wang@kaust.edu.sa |
| Pseudocode | Yes | Algorithm 1 Private and Robust Arm Elimination |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper discusses theoretical models and simulation results, but does not provide concrete access information for a publicly available or open dataset used for training. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper discusses experimental studies but does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. |