Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Pseudo-Label Reconstruction for Partial Multi-Label Learning
Authors: Yu Chen, Fang Li, Na Han, Guanbin Li, Hongbo Gao, Sixian Chan, Xiaozhao Fang
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments and analyses demonstrate that the proposed PML-PLR outperforms state-of-the-art methods. ... 4 Experiments 4.1 Datasets 4.2 Baselines and Implementation Details 4.3 Experimental Results 4.4 Further Analysis |
| Researcher Affiliation | Academia | 1Guangdong University of Technology 2Guangdong Polytechnic Normal University 3Sun Yat-sen University 4Institute of Advanced Technology, University of Science and Technology of China 5Zhejiang University Of Technology |
| Pseudocode | No | The paper describes the optimization steps in Section 3.6 'Optimization' using mathematical formulas but does not contain a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code for the methodology described, nor does it include any links to code repositories. |
| Open Datasets | Yes | To evaluate the generalization performance of our proposed PML-PLR method, a total of 25 datasets are used for comparative study. ... 1http://palm.seu.edu.cn/zhangml/ 2http://mulan.sourceforge.net/datasets.html |
| Dataset Splits | No | The paper states, 'Cross-validation is employed to select the optimal latent space dimension m.' for parameter selection, but it does not provide specific details on how the datasets were split (e.g., percentages or k-fold setup) for the main experimental evaluations presented in the results tables. |
| Hardware Specification | No | The paper does not provide any specific hardware details (such as GPU/CPU models, memory, or specific computing environments) used for running the 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 experiments. |
| Experiment Setup | Yes | The parameters α, β and λ in the PML-PLR are selected using grid search from {10 4, 10 3, 10 2, 10 1, 100}. |