This monograph develops a comprehensive statistical learning framework that is robust to
(distributional) perturbations in the data using Distributionally Robust Optimization (DRO) …

We consider logistic regression with arbitrary outliers in the covariate matrix. We propose a
new robust logistic regression algorithm, called RoLR, that estimates the parameter through …

The goal of collaborative filtering is to get accurate recommendations at the top of the list for
a set of users. From such a perspective, collaborative ranking based formulations with …

We study the stability of posterior predictive inferences to the specification of the likelihood
model and perturbations of the data generating process. In modern big data analyses, useful …

This dissertation develops a comprehensive statistical learning framework that is robust to
(distributional) perturbations in the data using Distributionally Robust Optimization (DRO) …

Recommendation systems have become an integral part of our everyday lives. Although
there have been many works focusing on recommendation quality, many real-world aspects …

This monograph develops a comprehensive statistical learning framework that is robust to
(distributional) perturbations in the data using Distributionally Robust Optimization (DRO) …