[psandersen]

Thanks for the reference, from a quick read it seems LogitBoost is a booster for ensembling models under a logistic loss.

I meant that the splitting point in a node in the decision trees that make up a random forest is itself a random variable that follows a distribution. Because its a smooth function (i.e. probability of splitting is 50% at the point and rises/falls smoothly) it should in principle be differentiable* so that the whole model can be trained by SGD and/or fit into an end to end learning pipeline with convolutional layers etc.

*Where I'm hazy, is how can a smooth probability function be differentiable when sampling. I'm brainstorming in the open here, will do some reading on stochastic neural networks.