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 Label Learning by Semantic Difference Maximization
Authors: Lei Feng, Bo An
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on artificial and real-world partial label datasets show that our approach significantly outperforms state-of-the-art counterparts. |
| Researcher Affiliation | Collaboration | 1School of Computer Science and Engineering, Nanyang Technological University, Singapore 2Alibaba-NTU Singapore Joint Research Institute, Singapore |
| Pseudocode | Yes | Algorithm 1 The SDIM Algorithm |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | Dataset ecoli dermatology vehicle usps (Table 1), Lost 1122 108 16 2.23 automatic face naming [Panis and Lanitis, 2014] (Table 2) |
| Dataset Splits | Yes | For each dataset, we perform ten-fold cross-validation, and report the resulting mean prediction accuracies and the standard deviations. For all the approaches, parameters are selected by five-fold cross-validation on the training set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | For our approach, λ is searched in {0.001, 0.005, , 0.5} and β is searched in {0.00001, 0.00005, 0.0001, , 0.1}. For all the approaches, parameters are selected by five-fold cross-validation on the training set. |