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Found 2 papers, 1 papers with code
Survival regression with accelerated failure time model in XGBoost
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.
Block-distributed Gradient Boosted Trees
As a result, we are able to reduce the training time for high-dimensional data, and allow more cost-effective scale-out without the need for expensive network communication.
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