Search Results for author:
Found 9 papers, 1 papers with code
Distributionally Robust Multiclass Classification and Applications in Deep Image Classifiers
We develop a Distributionally Robust Optimization (DRO) formulation for Multiclass Logistic Regression (MLR), which could tolerate data contaminated by outliers.
Distributionally Robust Multi-Output Regression Ranking
We show that this is equivalent to a regularized regression problem with a matrix norm regularizer.
Distributionally Robust Multiclass Classification and Applications in Deep Image Classifiers
We develop a Distributionally Robust Optimization (DRO) formulation for Multiclass Logistic Regression (MLR), which could tolerate data contaminated by outliers.
Distributionally Robust Learning
This monograph develops a comprehensive statistical learning framework that is robust to (distributional) perturbations in the data using Distributionally Robust Optimization (DRO) under the Wasserstein metric.
Robust Grouped Variable Selection Using Distributionally Robust Optimization
We propose a Distributionally Robust Optimization (DRO) formulation with a Wasserstein-based uncertainty set for selecting grouped variables under perturbations on the data for both linear regression and classification problems.
Robustified Multivariate Regression and Classification Using Distributionally Robust Optimization under the Wasserstein Metric
We develop Distributionally Robust Optimization (DRO) formulations for Multivariate Linear Regression (MLR) and Multiclass Logistic Regression (MLG) when both the covariates and responses/labels may be contaminated by outliers.
Selecting Optimal Decisions via Distributionally Robust Nearest-Neighbor Regression
This paper develops a prediction-based prescriptive model for optimal decision making that (i) predicts the outcome under each action using a robust nonlinear model, and (ii) adopts a randomized prescriptive policy determined by the predicted outcomes.
Learning Optimal Personalized Treatment Rules Using Robust Regression Informed K-NN
We develop a prediction-based prescriptive model for learning optimal personalized treatments for patients based on their Electronic Health Records (EHRs).
A Robust Learning Algorithm for Regression Models Using Distributionally Robust Optimization under the Wasserstein Metric
We present a Distributionally Robust Optimization (DRO) approach to estimate a robustified regression plane in a linear regression setting, when the observed samples are potentially contaminated with adversarially corrupted outliers.
