Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis
Authors: Tim Pearce, Jong-Hyeon Jeong, yichen jia, Jun Zhu
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 6 the algorithm s effectiveness is empirically demonstrated by benchmarking against alternative methods on simulated and real data. Careful ablations illuminate effects of the algorithm s hyperparameters. |
| Researcher Affiliation | Collaboration | Tim Pearce1,2, , Jong-Hyeon Jeong3, Yichen Jia3, Jun Zhu1 1Dept. of Comp. Sci. & Tech., NRist Center, Tsinghua-Bosch Joint ML Center, Tsinghua University 2Microsoft Research, 3University of Pittsburgh |
| Pseudocode | Yes | Algorithm 1 Sequential grid algorithm for NNs. Algorithm 2 CQRNN algorithm. |
| Open Source Code | Yes | Code: https://github.com/Tea Pearce/Censored_Quantile_Regression_NN. |
| Open Datasets | Yes | Table 2: Summary of all datasets used. The paper lists standard benchmark datasets like Housing, Protein, Wine, PHM, Surv MNIST, METABRIC, WHAS, SUPPORT, GBSG, TMBImmuno, Breast MSK, LGGGBM, and provides detailed characteristics for them, implying public availability. |
| Dataset Splits | No | Table 2 specifies 'Train' and 'Test' data sizes, but no explicit 'validation' dataset split or sample counts are provided in the main text or tables. |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU types, memory) used for experiments are provided in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x) are listed in the paper. |
| Experiment Setup | Yes | All experiments use fully-connected NNs with two hidden layers of 100 neurons, except for Surv MNIST, when three convolutional layers are used. Grid size M is set to 5, 9 or 19 depending on dataset size...using cy = 1.2 provided consistently reasonable results... Appendix B.1: For all experiments we use the Adam optimizer with a learning rate of 1e-3, and a batch size of 256 for all datasets except those with N>20000, where a batch size of 512 is used. We train for 200 epochs unless stated otherwise. |