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.
Implicit Relative Labeling-Importance Aware Multi-Label Metric Learning
Authors: Jun-Xiang Mao, Yong Rui, Min-Ling Zhang
AAAI 2025 | Venue PDF | LLM Run Details | Input Tokens: 17,621 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,681 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 | Comprehensive experiments on benchmark multi-label datasets validate the superiority of our proposed approach in learning effective similarity metrics between multi-label examples. |
| Researcher Affiliation | Collaboration | 1School of Computer Science and Engineering, Southeast University, Nanjing 210096, China ... EMAIL, ... EMAIL" and "4Lenovo Research, Lenovo Group Ltd., Beijing, China ... EMAIL |
| Pseudocode | No | The complete procedure of the proposed ILIA approach is summarized in Appendix A. (Appendix A is not provided in the given text.) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | In this paper, ten real-world multi-label datasets with diversified properties are employed for comparative studies. Table 1 summarizes the detailed characteristics of each benchmark dataset D, including the number of examples |D|, number of features dim(D), number of labels L(D), label cardinality LCard(D), and domain of datasets. 1 http://mulan.sourceforge.net/datasets.html 2 http://palm.seu.edu.cn/zhangml/Resources.htm#data |
| Dataset Splits | Yes | Ten-fold cross-validation is employed to evaluate the above compared approaches in this paper. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | For the proposed ILIA approach, we use the Polynomial kernel and set the parameters as follows: the trade-off parameters ยต = 10 3, ฮท = 10 2, ฮณ = 10 2, and the number of nearest neighbors k = 20. ... For KNN and MLKNN, the number of nearest neighbors is fixed to 10 for fair comparisons. |