Learning Survival Distribution with Implicit Survival Function
Authors: Yu Ling, Weimin Tan, Bo Yan
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that ISF outperforms the state-of-the-art methods in three public datasets and has robustness to the hyperparameter controlling estimation precision. To demonstrate performance of the proposed model compared with the state-of-the-art methods, experiments are built on several real-world datasets. Experimental results show that ISF outperforms the state-of-the-art methods. |
| Researcher Affiliation | Academia | School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Shanghai Collaborative Innovation Center of Intelligent Visual Computing, Fudan University, Shanghai, China. yling21@m.fudan.edu.cn, {wmtan, byan}@fudan.edu.cn |
| Pseudocode | No | The paper does not contain any sections explicitly labeled 'Pseudocode' or 'Algorithm', nor does it present structured algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/Bcai0797/ISF. |
| Open Datasets | Yes | CLINIC tracks patients clinic status [Knaus et al., 1995]. MUSIC is a user lifetime analysis containing about 1000 users with entire listening history [Jing and Smola, 2017]. METABRIC dataset contains gene expression profiles and clinical features of the breast cancer from 1,981 patients [Curtis et al., 2012]. |
| Dataset Splits | Yes | The training and testing split of CLINIC and MUSIC follows the setting of DRSA [Ren et al., 2019]. For METABRIC, 5-fold cross validation is applied following Deep Hit [Lee et al., 2018]. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments (e.g., CPU, GPU models, or cloud computing instances with their specifications). |
| Software Dependencies | No | The paper states 'ISF is implemented with Py Torch.' but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | Number of hidden units of E( ) defined in Eq. 10 and H( ) defined in Eq. 11 are corresponding set as {256, 512, 256} and {256, 256, 1} for all experiments. During training, we perform Adam optimizer. Models of the best CI is selected with variation in hyperparameters of learning rate {10 3, 10 4, 10 5}, weight of decay {10 3, 10 4, 10 5} and batch size {8, 16, 32, 64, 128, 256}. |