Leveraging Latent Label Distributions for Partial Label Learning
Authors: Lei Feng, Bo An
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on controlled UCI datasets as well as real-world datasets clearly show the effectiveness of the proposed approach. |
| Researcher Affiliation | Academia | Lei Feng and Bo An School of Computer Science and Engineering, Nanyang Technological University, Singapore feng0093@e.ntu.edu.sg, boan@ntu.edu.sg |
| Pseudocode | Yes | Algorithm 1 The LALO Algorithm |
| Open Source Code | Yes | Figures and code package for LALO are publicly available at: https://sites.google.com/site/ramber1995paper/publications |
| Open Datasets | Yes | 1These data sets are publicly avaible at: http://cse.seu.edu.cn/Personal Page/zhangml/Resources.htm#partial data |
| Dataset Splits | Yes | On each artificial and real-world dataset, ten runs of 50%/50% random train/test splits are performed |
| Hardware Specification | No | No specific hardware details are provided in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers are provided in the paper. |
| Experiment Setup | Yes | The parameters employed by LALO are set as k = 10, λ = 0.05, µ = 0.005. |