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.
Boosting Efficiency in Task-Agnostic Exploration through Causal Knowledge
Authors: Yupei Yang, Biwei Huang, Shikui Tu, Lei Xu
IJCAI 2024 | Venue PDF | LLM Run Details | Input Tokens: 16,471 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,203 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical experiments, on both synthetic data and realworld applications, further validate the benefits of causal exploration. The source code is available at https://github.com/CMACH508/Causal Exploration. (Abstract) ... To evaluate the effectiveness of causal exploration in complex scenarios, we conduct a series of experiments on both synthetic datasets and real-world applications, including the traffic light control task and Mu Jo Co control suites [Todorov et al., 2012]. (Section 6) |
| Researcher Affiliation | Academia | Yupei Yang1 , Biwei Huang2 , Shikui Tu1 and Lei Xu1,3 1Department of Computer Science and Engineering, Shanghai Jiao Tong University, China 2Halicio glu Data Science Institute (HDSI), University of California San Diego, USA 3Guangdong Institute of Intelligence Science and Technology, Zhuhai, China |
| Pseudocode | Yes | Algorithm 1 Task-agnostic Causal Exploration (Section 5, page 5) |
| Open Source Code | Yes | The source code is available at https://github.com/CMACH508/Causal Exploration. (Abstract) |
| Open Datasets | No | The paper refers to |
| Dataset Splits | No | The paper mentions a |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | Implementation details and more experimental results including the identified causal structures are given in Appendix D. (Section 6.1) ... We adopt the Adam optimizer with learning rate 3e-4, weight decay 1e-5. (Appendix D) |