Learning Safe Prediction for Semi-Supervised Regression
Authors: Yu-Feng Li, Han-Wen Zha, Zhi-Hua Zhou
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on a broad range of datasets validate the effectiveness of our proposal. |
| Researcher Affiliation | Academia | Yu-Feng Li, Han-Wen Zha, Zhi-Hua Zhou National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210023, China {liyf,zhouzh}@lamda.nju.edu.cn; zhahw12@gmail.com |
| Pseudocode | Yes | Algorithm 1 summarizes the pseudocode of the proposed method. |
| Open Source Code | Yes | 3http://lamda.nju.edu.cn/code/SAFER.ashx |
| Open Datasets | Yes | extensive experiments are conducted on a broad range of data sets2 (Table 1) ... 2https://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/ |
| Dataset Splits | No | The paper states 'For each data set, 5 and 10 labeled instances are randomly selected and the rest ones are unlabeled data' for evaluation, but does not explicitly mention a separate 'validation' dataset split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like 'MOSEK package' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For the Self-k NN method, the Euclidean distance is used and k is set to 3. The maximum number of iterations is set to 5 and further increasing it does not improve performance. For the Self-LS method, the parameters related to the importance for the labeled and unlabeled instances are set to 1 and 0.1, respectively. |