Ruidi Chen
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View article: MicroRNA-155-5p rs767649 Polymorphism is Associated with Susceptibility to Essential Hypertension in the Chinese Tibetan Population in the Gannan Area
MicroRNA-155-5p rs767649 Polymorphism is Associated with Susceptibility to Essential Hypertension in the Chinese Tibetan Population in the Gannan Area Open
Background The latest evidence has demonstrated the aberrant expression and diagnostic meaning of microRNA-155-5p in hypertension. Rs767649 is a common polymorphism in miR-155-5p and can mediate its expression. Objective A case–control stu…
View article: Distributionally robust learning-to-rank under the Wasserstein metric
Distributionally robust learning-to-rank under the Wasserstein metric Open
Despite their satisfactory performance, most existing listwise Learning-To-Rank (LTR) models do not consider the crucial issue of robustness. A data set can be contaminated in various ways, including human error in labeling or annotation, …
View article: Distributionally Robust Multiclass Classification and Applications in Deep Image Classifiers
Distributionally Robust Multiclass Classification and Applications in Deep Image Classifiers Open
We develop a Distributionally Robust Optimization (DRO) formulation for Multiclass Logistic Regression (MLR), which could tolerate data contaminated by outliers. The DRO framework uses a probabilistic ambiguity set defined as a ball of dis…
View article: Profile of Folate in Breast Milk from Chinese Women over 1–400 Days Postpartum
Profile of Folate in Breast Milk from Chinese Women over 1–400 Days Postpartum Open
Folate is an essential nutrient for growth in early life. This study aimed to determine the levels and compositions of folate in Chinese breast milk samples. This study was part of the Maternal Nutrition and Infant Investigation (MUAI) stu…
View article: Distributionally Robust Multiclass Classification and Applications in Deep CNN Image Classifiers
Distributionally Robust Multiclass Classification and Applications in Deep CNN Image Classifiers Open
We develop a Distributionally Robust Optimization (DRO) formulation for Multiclass Logistic Regression (MLR), which could tolerate data contaminated by outliers. The DRO framework uses a probabilistic ambiguity set defined as a ball of dis…
View article: Distributionally Robust Multiclass Classification and Applications in Deep Image Classifiers
Distributionally Robust Multiclass Classification and Applications in Deep Image Classifiers Open
We develop a Distributionally Robust Optimization (DRO) formulation for Multiclass Logistic Regression (MLR), which could tolerate data contaminated by outliers. The DRO framework uses a probabilistic ambiguity set defined as a ball of dis…
View article: Distributionally Robust Multi-Output Regression Ranking
Distributionally Robust Multi-Output Regression Ranking Open
Despite their empirical success, most existing listwiselearning-to-rank (LTR) models are not built to be robust to errors in labeling or annotation, distributional data shift, or adversarial data perturbations. To fill this gap, we introdu…
View article: Distributionally Robust Learning
Distributionally Robust Learning Open
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. Beginning with fundamental p…
View article: Distributionally Robust Learning
Distributionally Robust Learning Open
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. Beginning with fundamental p…
View article: Robust Grouped Variable Selection Using Distributionally Robust Optimization
Robust Grouped Variable Selection Using Distributionally Robust Optimization Open
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. The result…
View article: Robustified Multivariate Regression and Classification Using Distributionally Robust Optimization under the Wasserstein Metric
Robustified Multivariate Regression and Classification Using Distributionally Robust Optimization under the Wasserstein Metric Open
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. The DRO fra…
View article: Learning from Past Bids to Participate Strategically in Day-Ahead Electricity Markets
Learning from Past Bids to Participate Strategically in Day-Ahead Electricity Markets Open
We consider the process of bidding by electricity suppliers in a day-ahead market context where each supplier bids a linear non-decreasing function of her generating capacity with the goal of maximizing her individual profit given other co…
View article: Learning Optimal Personalized Treatment Rules Using Robust Regression Informed K-NN
Learning Optimal Personalized Treatment Rules Using Robust Regression Informed K-NN Open
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: (i) predicting future outcomes under each possible therap…
View article: A Robust Learning Approach for Regression Models Based on Distributionally Robust Optimization.
A Robust Learning Approach for Regression Models Based on Distributionally Robust Optimization. Open
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. Our a…
View article: A Robust Learning Algorithm for Regression Models Using Distributionally Robust Optimization under the Wasserstein Metric
A Robust Learning Algorithm for Regression Models Using Distributionally Robust Optimization under the Wasserstein Metric Open
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. Our a…
View article: Outlier detection using distributionally robust optimization under the Wasserstein metric
Outlier detection using distributionally robust optimization under the Wasserstein metric Open
We present a Distributionally Robust Optimization (DRO) approach to outlier detection in a linear regression setting, where the closeness of probability distributions is measured using the Wasserstein metric. Training samples contaminated …