Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP
Authors: Zihan Zhang, Jiaqi Yang, Xiangyang Ji, Simon S. Du
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We have no experiments. |
| Researcher Affiliation | Academia | Zihan Zhang Tsinghua University zihan-zh17@mails.tsinghua.edu.cn Jiaqi Yang Tsinghua University yangjq17@gmail.com Xiangyang Ji Tsinghua University xyji@tsinghua.edu.cn Simon S. Du University of Washington ssdu@cs.washington.edu |
| Pseudocode | Yes | Algorithm 1 VOFUL: Variance-Aware Optimism in the Face of Uncertainty for Linear Bandits |
| Open Source Code | No | We have no experiments. The paper does not contain any statement about releasing source code or links to repositories. |
| Open Datasets | No | We have no experiments. The paper does not mention using or providing access to any dataset. |
| Dataset Splits | No | We have no experiments. The paper does not specify any dataset splits. |
| Hardware Specification | No | We have no experiments. The paper does not contain any information about hardware specifications used for experiments. |
| Software Dependencies | No | We have no experiments. The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | No | We have no experiments. The paper does not provide details about experimental setup, hyperparameters, or training configurations. |