Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP
Authors: Zihan Zhang, Jiaqi Yang, Xiangyang Ji, Simon S. Du
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 20,285 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,305 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We have no experiments. |
| Researcher Affiliation | Academia | Zihan Zhang Tsinghua University EMAIL Jiaqi Yang Tsinghua University EMAIL Xiangyang Ji Tsinghua University EMAIL Simon S. Du University of Washington EMAIL |
| 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. |