6 citations · 6 across the 1 of their papers we have counts for
3 papers
stat.ML2020
Machine Learning's Dropout Training is Distributionally Robust Optimal
Jose Blanchet, Yang Kang, Jose Luis Montiel Olea +2
This paper shows that dropout training in Generalized Linear Models is the minimax solution of a two-player, zero-sum game where an adversarial nature corrupts a statistician's cov…
stat.ML2019
A Distributionally Robust Boosting Algorithm
Jose Blanchet, Yang Kang, Fan Zhang +1
Distributionally Robust Optimization (DRO) has been shown to provide a flexible framework for decision making under uncertainty and statistical estimation. For example, recent work…
math.ST2017★ 6 cited
Distributionally Robust Groupwise Regularization Estimator
Jose Blanchet, Yang Kang
Regularized estimators in the context of group variables have been applied successfully in model and feature selection in order to preserve interpretability. We formulate a Distrib…