Posistive-Unlabeled Learning via Optimal Transport and Margin Distribution
Authors: Nan Cao, Teng Zhang, Xuanhua Shi, Hai Jin
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive empirical studies on both synthetic and real-world data sets demonstrate the superiority of our proposed method. |
| Researcher Affiliation | Academia | Nan Cao , Teng Zhang , Xuanhua Shi and Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, China {nan cao, tengzhang, xhshi, hjin}@hust.edu.cn |
| Pseudocode | Yes | Algorithm 1 Class prior estimation |
| Open Source Code | No | No statement regarding the release or availability of open-source code for the described methodology was found in the paper. |
| Open Datasets | Yes | For real-world data sets, we utilize eight data sets from the UCI Machine Learning Repository. Their basic statistics are listed in Table 1. |
| Dataset Splits | No | The paper states: 'All data sets are randomly divided into training and test set with ratio 7:3', but does not explicitly mention a separate validation dataset split. |
| Hardware Specification | No | No specific details about the hardware (e.g., GPU/CPU models, memory, or cloud computing resources) used for running the experiments were provided in the paper. |
| Software Dependencies | No | The paper mentions the use of 'off-the-shelf QP solvers' and 'off-the-shelf LP solvers', but does not provide specific software names or version numbers for any ancillary software dependencies. |
| Experiment Setup | Yes | For CAPU and LDCE, their two trade-off hyperparameters are tuned from {2 5, . . . , 25} and {10 3, . . . , 103} respectively. The balancing hyperparameters in PULD are tuned from {10 3, . . . , 103}. As for our proposed method, we set the number of nearest neighbors N = 10, and the width of RBF kernel is selected from {2 10, 2 9, . . . , 23}. We tune ODM hyperparameters µ and θ from the set {0.1, 0.2, . . . , 0.9}, the threshold σ is set as min{1, 10/p}, and the balancing hyperparameters are selected from {10 3, . . . , 103}. |