Fix typos, addressing issues #2212 and #3090 (#3105)

doc/tutorials/dart.md

@@ -3,7 +3,7 @@ DART booster
 [XGBoost](https://github.com/dmlc/xgboost)) mostly combines a huge number of regression trees with a small learning rate.
 In this situation, trees added early are significant and trees added late are unimportant.
 
-Rasmi et al. proposed a new method to add dropout techniques from the deep neural net community to boosted trees, and reported better results in some situations.
+Vinayak and Gilad-Bachrach proposed a new method to add dropout techniques from the deep neural net community to boosted trees, and reported better results in some situations.
 
 This is a instruction of new tree booster `dart`.