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.
Embedding-based Representation of Categorical Data by Hierarchical Value Coupling Learning
Authors: Songlei Jian, Longbing Cao, Guansong Pang, Kai Lu, Hang Gao
IJCAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 16,302 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,986 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 | Substantial experiments show that (1) CDE significantly outperforms three popular embedding methods and three stateof-the-art coupled similarity measures in terms of F-score for clustering on 10 real-world data sets with different value coupling complexities |
| Researcher Affiliation | Academia | Advanced Analytics Institute, University of Technology Sydney, Australia Science and Technology on Parallel and Distributed Processing Laboratory, State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, China |
| Pseudocode | Yes | Algorithm 1 Value Embedding (D, α, β) |
| Open Source Code | No | No explicit statement about providing open-source code or a direct link was found. |
| Open Datasets | Yes | We use ten real-world UCI data sets from different domains for the experiments. |
| Dataset Splits | No | No specific details about training, validation, and test dataset splits were provided, such as percentages, sample counts, or predefined split references. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for experiments were mentioned. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., library names with versions) were mentioned. |
| Experiment Setup | Yes | We set parameter α = 10 in CDE and parameter β = 10^-10 in PCA used by CDE and 0-1P. In COS, DILCA and ALGO, we use the default parameters in their original papers. |