  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.
