Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Toward Conditional Distribution Calibration in Survival Prediction
Authors: Shi-ang Qi, Yakun Yu, Russell Greiner
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide asymptotic theoretical guarantees for both marginal and conditional calibration and test it extensively across 15 diverse real-world datasets, demonstrating the method s practical effectiveness and versatility in various settings. |
| Researcher Affiliation | Academia | Shi-ang Qi 1, Yakun Yu 2, Russell Greiner 1 3 1Computing Science, University of Alberta, Edmonton, Canada 2Electrical Computer Engineering, University of Alberta, Edmonton, Canada 3Alberta Machine Intelligence Institute, Edmonton, Canada |
| Pseudocode | Yes | The pseudo-code for implementing the Ci POT process with censoring is outlined in Algorithm 1 in Appendix B. |
| Open Source Code | Yes | The implementation of Ci POT method, worst-slab distribution calibration score, and the code to reproduce all experiments in this section are available at https://github.com/shi-ang/ Make Survival Calibrated Again. |
| Open Datasets | Yes | We use 15 datasets to test the effectiveness of our method. Table 3 in Appendix E.1 summarizes the dataset statistics, and Appendix E.1 also contains details of preprocessing steps, KM curves, and histograms of event/censor times. |
| Dataset Splits | Yes | We divided the data into a training set (90%) and a testing set (10%) using a stratified split to balance time ti and censor indicator ÎŽi. We also reserved a balanced 10% validation subset from the training data for hyperparameter tuning and early stopping. |
| Hardware Specification | No | NA |
| Software Dependencies | No | It is implemented in lifelines packages [59]. |
| Experiment Setup | Yes | Full hyperparameter details for NN-Based survival baselines In the experiments, all neural network-based methods (including N-MTLR, Deep Surv, Deep Hit, Cox Time, and CQRNN) used the same architecture and optimization procedure. Training maximum epoch: 10000 Early stop patients: 50 Optimizer: Adam Batch size: 256 Learning rate: 1e-3 Learning rate scheduler: Cosine Annealing LR Learning rate minimum: 1e-6 Weight decay: 0.1 NN architecture: [64, 64] Activation function: ReLU Dropout rate: 0.4 Full hyperparameter details for CSD and Ci POT Interpolation: {Linear, PCHIP} Extrapolation: Linear Monotonic method: {Ceiling, Flooring, Booststraping} Number percentile: {9, 19, 39, 49} Conformal set: {Validation set, Training set + Validation set} Repetition parameter: {3, 5, 10, 100, 1000} |