Dropout regularization reduces overfitting in Neural networks, especially deep belief networks (srivastava14a). It also has the opportunity to accelerate learning because individual learning iterations are on a reduced set of the model.
The gradient boosted tree (like those xgboost or gbm) is known for being an excellent ensemble learner, but one that suffers from over-fitting.
Is there an analog to dropout regression that is used in GBM learning?
Does "subsampling" or "stochastic gradient learning" relate to this - does it count as a version of dropout regression?
I think that the stochastic gradient learning is about "randomly disabling inputs" and not "randomly disabling individual learners".