Why isn't dimension sampling used with gradient boosting machines (GBM)? GBMs, like random forests, build each tree on a different sample of the dataset and hence, going by the spirit of ensemble models, produce higher accuracies. However, I have not seen GBM being used with dimension sampling at every split of the tree like is common practice with random forests. 

Are there some tests that show that dimensional sampling with GBM would decrease its accuracy because of which this is avoided, either in literature form or in practical experience?
sklearn's GradientBoostingClassifier / GradientBoostingRegressor have a max_features parameter and XGBoost has colsample_bylevel and colsample_bytree parameters that control how many features are sampled for each tree / split.