15 citations · 27 across the 8 of their papers we have counts for
3 papers · 1 filter
Unbiased Gradient Estimation for Distributionally Robust Learning
Soumyadip Ghosh, Mark Squillante
Seeking to improve model generalization, we consider a new approach based on distributionally robust learning (DRL) that applies stochastic gradient descent to the outer minimizati…
Efficient Stochastic Gradient Descent for Learning with Distributionally Robust Optimization
Soumyadip Ghosh, Mark Squillante, Ebisa Wollega
Distributionally robust optimization (DRO) problems are increasingly seen as a viable method to train machine learning models for improved model generalization. These min-max formu…
A General Family of Robust Stochastic Operators for Reinforcement Learning
Yingdong Lu, Mark S. Squillante, Chai Wah Wu
We consider a new family of operators for reinforcement learning with the goal of alleviating the negative effects and becoming more robust to approximation or estimation errors. V…