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 with Self-Guided Retraining
Authors: Lei Feng, Bo An3542-3549
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on synthesized and real-world datasets demonstrate that the proposed approach significantly outperforms the state-of-the-art partial label learning approaches. |
| Researcher Affiliation | Collaboration | 1School of Computer Science and Engineering, Nanyang Technological University, Singapore 2Alibaba-NTU Singapore Joint Research Institute, Singapore |
| Pseudocode | Yes | The pseudo code of SURE is presented in Algorithm 1. |
| 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 a link to a code repository. |
| Open Datasets | Yes | Following the widely-used controlling protocol (Cour, Sapp, and Taskar 2011; Liu and Dietterich 2012; Zhang and Yu 2015; Wu and Zhang 2018; Feng and An 2018; Wang and Zhang 2018), each UCI dataset can be used to generate artificial partial label datasets. These datasets are publicly available at: http://cse.seu.edu.cn/Personal Page/zhangml/ |
| Dataset Splits | Yes | Parameters for each algorithm are selected by five-fold cross-validation on the training set. For each dataset, ten-fold cross-validation is performed where mean prediction accuracies and the standard deviations are recorded. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | The two parameters λ and β for SURE are chosen from {0.001, 0.01, 0.05, 0.1, 0.3, 0.5, 1}. Parameters for each algorithm are selected by five-fold cross-validation on the training set. In this paper, Gaussian kernel function κ(xi, xj) = exp( xi xj 2 2 /(2σ2)) is employed with σ set to the averaged pairwise distances of instances. |