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..
Partial Multi-Label Learning via Large Margin Nearest Neighbour Embeddings
Authors: Xiuwen Gong, Dong Yuan, Wei Bao6729-6736
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on artificial and real-world datasets demonstrate the superiorities of the proposed PML-LMNNE. |
| Researcher Affiliation | Collaboration | Xiuwen Gong 1, 2, Dong Yuan 1, Wei Bao 1 1 Faculty of Engineering, The University of Sydney 2 Hunan Huishiwei Intelligent Technology Co., Ltd. |
| Pseudocode | Yes | Algorithm 1: PML-LMNNE Algorithm |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the described methodology is open-source or publicly available. |
| Open Datasets | Yes | http://mulan.sourceforge.net/datasets-mlc.html |
| Dataset Splits | Yes | For PML-LMNNE, we initialize the regularization parameter C by 10-fold cross-validation over the range 10 2, . . . , 102 and use Euclidean metric to find the nearest neighbour in initializing the training process. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like 'Libsvm', but does not specify version numbers for any key software components or libraries. |
| Experiment Setup | Yes | For PML-LMNNE, we initialize the regularization parameter C by 10-fold cross-validation over the range 10 2, . . . , 102 and use Euclidean metric to find the nearest neighbour in initializing the training process. |