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..
Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization
Authors: Wei Wang, Min-Ling Zhang
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on synthetic as well as real-world data sets clearly validate the effectiveness of the proposed semi-supervised partial label learning approach. |
| Researcher Affiliation | Academia | Wei Wang Min-Ling Zhang School of Computer Science and Engineering, Southeast University, Nanjing 210096, China Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China EMAIL |
| Pseudocode | Yes | Table 1: Pseudo-code of PARM. |
| Open Source Code | No | The paper does not provide a specific link to source code or explicitly state that the code for the methodology is being released or is publicly available. |
| Open Datasets | Yes | Table 2 summarizes characteristics of the experimental data sets used in this paper. Following the widely-used experimental protocol in partial label learning [6, 7, 8, 11], synthetic PL data sets are generated from multi-class UCI data sets with controlling parameter r. Furthermore, five real-world PL data sets from different task domains have also been employed for experimental studies, including Lost [8], LYN10, LYN20 [12] for automatic face naming, Mirflickr[13] for web image classification, and Bird Song [4] for bird song classification. |
| Dataset Splits | Yes | On each data set, ten-fold cross validation is performed whose mean accuracy as well as standard deviation are recorded for all comparing approaches. |
| Hardware Specification | No | The paper does not provide specific hardware details (like CPU/GPU models or memory amounts) used for running the experiments. It only mentions: "We thank the Big Data Center of Southeast University for providing the facility support on the numerical calculations in this paper." |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, specific libraries) needed to replicate the experiment. |
| Experiment Setup | Yes | In this paper, σ, k and α are fixed to be 1, 8 and 0.95 respectively. ...the regularization parameters λ and µ for PARM are chosen among {0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10} via cross-validation on training set and γ = 0.01. |