21 citations · 59 across the 14 of their papers we have counts for
6 papers · 1 filter
Distributionally Robust Learning
Ruidi Chen, Ioannis Ch. Paschalidis
This monograph develops a comprehensive statistical learning framework that is robust to (distributional) perturbations in the data using Distributionally Robust Optimization (DRO)…
Robust Grouped Variable Selection Using Distributionally Robust Optimization
Ruidi Chen, Ioannis Ch. Paschalidis
We propose a Distributionally Robust Optimization (DRO) formulation with a Wasserstein-based uncertainty set for selecting grouped variables under perturbations on the data for bot…
Robustified Multivariate Regression and Classification Using Distributionally Robust Optimization under the Wasserstein Metric
Ruidi Chen, Ioannis Ch. Paschalidis
We develop Distributionally Robust Optimization (DRO) formulations for Multivariate Linear Regression (MLR) and Multiclass Logistic Regression (MLG) when both the covariates and re…
Convergence of Parameter Estimates for Regularized Mixed Linear Regression Models
Taiyao Wang, Ioannis Ch. Paschalidis
We consider {\em Mixed Linear Regression (MLR)}, where training data have been generated from a mixture of distinct linear models (or clusters) and we seek to identify the correspo…
Learning Optimal Personalized Treatment Rules Using Robust Regression Informed K-NN
Ruidi Chen, Ioannis Paschalidis
We develop a prediction-based prescriptive model for learning optimal personalized treatments for patients based on their Electronic Health Records (EHRs). Our approach consists of…
Sequential Dynamic Decision Making with Deep Neural Nets on a Test-Time Budget
Henghui Zhu, Feng Nan, Ioannis Paschalidis +1
Deep neural network (DNN) based approaches hold significant potential for reinforcement learning (RL) and have already shown remarkable gains over state-of-art methods in a number…