30 citations · 138 across the 35 of their papers we have counts for
9 papers · 1 filter
Mean-Covariance Robust Risk Measurement
Viet Anh Nguyen, Soroosh Shafiee, Damir Filipović +1
We introduce a universal framework for mean-covariance robust risk measurement and portfolio optimization. We model uncertainty in terms of the Gelbrich distance on the mean-covari…
Adversarial Regression with Doubly Non-negative Weighting Matrices
Tam Le, Truyen Nguyen, Makoto Yamada +2
Many machine learning tasks that involve predicting an output response can be solved by training a weighted regression model. Unfortunately, the predictive power of this type of mo…
Statistical Analysis of Wasserstein Distributionally Robust Estimators
Jose Blanchet, Karthyek Murthy, Viet Anh Nguyen
We consider statistical methods which invoke a min-max distributionally robust formulation to extract good out-of-sample performance in data-driven optimization and learning proble…
Testing Group Fairness via Optimal Transport Projections
Nian Si, Karthyek Murthy, Jose Blanchet +1
We present a statistical testing framework to detect if a given machine learning classifier fails to satisfy a wide range of group fairness notions. The proposed test is a flexible…
Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts
Bahar Taskesen, Man-Chung Yue, Jose Blanchet +2
Least squares estimators, when trained on a few target domain samples, may predict poorly. Supervised domain adaptation aims to improve the predictive accuracy by exploiting additi…
Principal Component Hierarchy for Sparse Quadratic Programs
Robbie Vreugdenhil, Viet Anh Nguyen, Armin Eftekhari +1
We propose a novel approximation hierarchy for cardinality-constrained, convex quadratic programs that exploits the rank-dominating eigenvectors of the quadratic matrix. Each level…
Robustifying Conditional Portfolio Decisions via Optimal Transport
Viet Anh Nguyen, Fan Zhang, Shanshan Wang +3
We propose a data-driven portfolio selection model that integrates side information, conditional estimation and robustness using the framework of distributionally robust optimizati…
Wasserstein Robust Classification with Fairness Constraints
Yijie Wang, Viet Anh Nguyen, Grani A. Hanasusanto
We propose a distributionally robust classification model with a fairness constraint that encourages the classifier to be fair in view of the equality of opportunity criterion. We…
Time-Series Imputation with Wasserstein Interpolation for Optimal Look-Ahead-Bias and Variance Tradeoff
Jose Blanchet, Fernando Hernandez, Viet Anh Nguyen +2
Missing time-series data is a prevalent practical problem. Imputation methods in time-series data often are applied to the full panel data with the purpose of training a model for…