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.
The Limits of Transfer Reinforcement Learning with Latent Low-rank Structure
Authors: Tyler Sam, Yudong Chen, Christina Yu
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 64,571 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,506 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 | There are no experiments in this paper. |
| Researcher Affiliation | Academia | Tyler Sam Cornell University EMAIL Yudong Chen University of Wisconsin-Madison EMAIL Christina Lee Yu Cornell University EMAIL |
| Pseudocode | Yes | Algorithm 1 Source Phase; Algorithm 2 Target Phase: LSVI-UCB-(S, S, d) |
| Open Source Code | No | There is no data or code used in this paper. |
| Open Datasets | No | The paper does not conduct experiments with datasets; therefore, it does not specify any training datasets or their public availability. |
| Dataset Splits | No | The paper does not conduct experiments; therefore, it does not provide validation dataset splits. |
| Hardware Specification | No | The paper does not conduct experiments; therefore, it does not describe the hardware used. |
| Software Dependencies | No | The paper does not conduct experiments; therefore, it does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper does not conduct experiments; therefore, it does not provide details about an experimental setup. |