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.
Learning to Learn Graph Topologies
Authors: Xingyue Pu, Tianyue Cao, Xiaoyun Zhang, Xiaowen Dong, Siheng Chen
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 18,356 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,761 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 | Experiments on both synthetic and real-world data demonstrate that our model is more efficient than classic iterative algorithms in learning a graph with specific topological properties. |
| Researcher Affiliation | Academia | Xingyue Pu University of Oxford EMAIL Tianyue Cao Xiaoyun Zhang Shanghai Jiao Tong University EMAIL Xiaowen Dong University of Oxford EMAIL Siheng Chen Shanghai Jiao Tong University EMAIL |
| Pseudocode | Yes | Algorithm 1 PDS Algorithm 2 Unrolling Net (L2G) |
| Open Source Code | Yes | We also release the code for implementation 4. (Footnote 4: https://github.com/xpuoxford/L2G-neurips2021) |
| Open Datasets | Yes | We use the daily returns of stocks obtained from Yahoo Finance^5. (Footnote 5: https://pypi.org/project/yahoofinancials/) We collect data from the dataset^6, which contains 539 subjects with autism spectrum disorder and 573 typical controls. (Footnote 6: http://preprocessed-connectomes-project.org/abide/) |
| Dataset Splits | No | The paper mentions training and testing but does not provide specific details on a validation split (e.g., percentages or sample counts) in the main text. |
| Hardware Specification | No | The paper does not specify the exact hardware used for experiments (e.g., specific GPU/CPU models or cloud instances) in the main text. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | No | The paper states "More implementation details, e.g. model configurations and training settings, can be found in Appendix." (Section 5), indicating that these specific details are not present in the main text. |