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.
Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition
Authors: Tiancheng Jin, Haipeng Luo
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 16,113 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,140 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 | This work is mostly theoretical, with no negative outcomes. |
| Researcher Affiliation | Academia | Tiancheng Jin University of Southern California EMAIL Haipeng Luo University of Southern California EMAIL |
| Pseudocode | Yes | Our final algorithm is shown in Algorithm 1. |
| Open Source Code | No | The paper is theoretical and does not mention releasing any code or provide links to a repository. |
| Open Datasets | No | The paper is theoretical and does not involve empirical evaluation on datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation or dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiment setup details such as hyperparameters or training configurations. |