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..
Learning With Incomplete Labels
Authors: Yingming Li, Zenglin Xu, Zhongfei Zhang
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluations on three benchmark datasets demonstrate that ICVL and ICVL-OD outstand with superior performances in comparison with the competing methods. |
| Researcher Affiliation | Academia | College of Information Science & Electronic Engineering, Zhejiang University, China School of Computer Science and Engineering, University of Electronic Science and Technology of China |
| Pseudocode | Yes | Algorithm 1: ICVL Algorithm |
| Open Source Code | No | The paper does not provide an explicit statement about releasing the source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | All datasets are obtained from http://mulan.sourceforge.net/datasets-mlc.html. |
| Dataset Splits | Yes | On the Enron and Birds datasets, we follow the experimental setup used in Mulan. Since there is no fixed split in the Bookmarks dataset in Mulan, we use a fixed training set of 80% of the data, and evaluate the performance of our predictions on the fixed test set of 20% of the data. |
| 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 describes algorithms and optimization methods but does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | No | The paper mentions regularization parameters and a label dropout probability but does not provide specific values for these or other hyperparameters (e.g., learning rate, batch size, epochs) or detailed training configurations in the main text. |