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.
Regret Analysis of Average-Reward Unichain MDPs via an Actor-Critic Approach
Authors: Swetha Ganesh, Vaneet Aggarwal
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 33,139 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 1,806 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 | The experimental results are not provided, since the focus of the paper is on theoretical sample complexity analysis. |
| Researcher Affiliation | Academia | Swetha Ganesh Purdue University, USA EMAIL Vaneet Aggarwal Purdue University, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Natural Actor-Critic with Batching |
| Open Source Code | No | The paper does not include experiments requiring code. |
| Open Datasets | No | The paper does not include experiments. |
| Dataset Splits | No | Our work is theoretical, and hence there are no experiments. |
| Hardware Specification | No | The paper does not include experiments. |
| Software Dependencies | No | The paper does not include experiments requiring code. |
| Experiment Setup | No | Our work is theoretical, and hence there are no experiments. |