# Survival regression with accelerated failure time model in XGBoost

arXiv: https://arxiv.org/abs/2006.04920 · HTML: https://mlsanity.com/paper/2006.04920/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2020.

## Built on by (most cited first, 30 of 37)

- [SurvivalGAN: Generating Time-to-Event Data for Survival Analysis](https://mlsanity.com/paper/2302.12749/) (2023)
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- [Conditional Mean and Variance Estimation via textitk-NN Algorithm with Automated Variance Selection](https://mlsanity.com/paper/2402.01635/) (2024)
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- [Penalty Learning for Optimal Partitioning using Multilayer Perceptron](https://mlsanity.com/paper/2408.00856/) (2024)
- [Optimizing ROC Curves with a Sort-Based Surrogate Loss Function for Binary Classification and Changepoint Detection](https://mlsanity.com/paper/2107.01285/) (2021)
- [EsmamDS: A more diverse exceptional survival model mining approach](https://mlsanity.com/paper/2109.02610/) (2021)
- [A copula-based boosting model for time-to-event prediction with dependent censoring](https://mlsanity.com/paper/2210.04869/) (2022)
- [Interpretable estimation of the risk of heart failure hospitalization from a 30-second electrocardiogram](https://mlsanity.com/paper/2211.00819/) (2022)
- [Discrimination, calibration, and point estimate accuracy of GRU-D-Weibull architecture for real-time individualized endpoint prediction](https://mlsanity.com/paper/2212.09606/) (2022)
- [A Statistical Learning Take on the Concordance Index for Survival Analysis](https://mlsanity.com/paper/2302.12059/) (2023)
- [Multi-modal Heart Failure Risk Estimation based on Short ECG and Sampled Long-Term HRV](https://mlsanity.com/paper/2403.15408/) (2024)
- [SurvBETA: Ensemble-Based Survival Models Using Beran Estimators and Several Attention Mechanisms](https://mlsanity.com/paper/2412.07638/) (2024)
- [Nonlinear Sparse Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data](https://mlsanity.com/paper/2502.18756/) (2025)
- [Interval Regression: A Comparative Study with Proposed Models](https://mlsanity.com/paper/2503.02011/) (2025)
- [Functional Decomposition and Shapley Interactions for Interpreting Survival Models](https://mlsanity.com/paper/2602.16505/) (2026)
- [Benchmarking Temporal Web3 Intelligence: Lessons from the FinSurvival 2025 Challenge](https://mlsanity.com/paper/2602.23159/) (2026)
- [Cello: Efficient Computer Systems Optimization with Predictive Early Termination and Censored Regression](https://mlsanity.com/paper/2204.04831/) (2022)
- [GRU-D-Weibull: A Novel Real-Time Individualized Endpoint Prediction](https://mlsanity.com/paper/2308.07452/) (2023)
- [A network-constrain Weibull AFT model for biomarkers discovery](https://mlsanity.com/paper/2402.18242/) (2024)
- [Analyzing Economic Convergence Across the Americas: A Survival Analysis Approach to GDP per Capita Trajectories](https://mlsanity.com/paper/2404.04282/) (2024)
- [Predicting Deterioration in Mild Cognitive Impairment with Survival Transformers, Extreme Gradient Boosting and Cox Proportional Hazard Modelling](https://mlsanity.com/paper/2409.16231/) (2024)
- [Efficient line search for optimizing Area Under the ROC Curve in gradient descent](https://mlsanity.com/paper/2410.08635/) (2024)
- [Learning Penalty for Optimal Partitioning via Automatic Feature Extraction](https://mlsanity.com/paper/2505.07413/) (2025)
- [Ensemble-Based Survival Models with the Self-Attended Beran Estimator Predictions](https://mlsanity.com/paper/2506.07933/) (2025)
- [Reduction Techniques for Survival Analysis](https://mlsanity.com/paper/2508.05715/) (2025)
- [Optimizing Prognostic Biomarker Discovery in Pancreatic Cancer Through Hybrid Ensemble Feature Selection and Multi-Omics Data](https://mlsanity.com/paper/2509.02648/) (2025)

## Abstract (arXiv)

Survival regression is used to estimate the relation between time-to-event and feature variables, and is important in application domains such as medicine, marketing, risk management and sales management. Nonlinear tree based machine learning algorithms as implemented in libraries such as XGBoost, scikit-learn, LightGBM, and CatBoost are often more accurate in practice than linear models. However, existing state-of-the-art implementations of tree-based models have offered limited support for survival regression. In this work, we implement loss functions for learning accelerated failure time (AFT) models in XGBoost, to increase the support for survival modeling for different kinds of label censoring. We demonstrate with real and simulated experiments the effectiveness of AFT in XGBoost with respect to a number of baselines, in two respects: generalization performance and training speed. Furthermore, we take advantage of the support for NVIDIA GPUs in XGBoost to achieve substantial speedup over multi-core CPUs. To our knowledge, our work is the first implementation of AFT that utilizes the processing power of NVIDIA GPUs. Starting from the 1.2.0 release, the XGBoost package natively supports the AFT model. The addition of AFT in XGBoost has had significant impact in the open source community, and a few statistics packages now utilize the XGBoost AFT model.