30 citations · 138 across the 35 of their papers we have counts for
36 papers
Tight Bounds for Data-driven Multiple Hyper-parameter Tuning with Structured Loss Function
Anh Tuan Nguyen, Viet Anh Nguyen
Data-driven algorithm design frames hyperparameter tuning as a statistical learning problem, but establishing generalization guarantees remains challenging due to the implicit, non…
Provably Data-driven Lagrangian Relaxation for Mixed Integer Linear Programming
Tung Quoc Le, Anh Tuan Nguyen, Viet Anh Nguyen
Lagrangian Relaxation (LR) is a powerful technique for solving large-scale Mixed Integer Linear Programming (MILP), particularly those with decomposable structures, such as vehicle…
Selective Ambulance Dispatch Under Contextual Travel-Time Uncertainty
Zikun Lin, Daniel Zhuoyu Long, Viet Anh Nguyen
Ambulance response is time-critical in out-of-hospital cardiac arrest (OHCA), where dispatchers must balance timely arrivals with limited fleet capacity. Static territories and det…
Wasserstein Projection Tests: Higher-Order Asymptotics and Connections to Empirical Likelihood
Sirui Lin, Jose Blanchet, Peter Glynn +1
Tests of moment restrictions can be constructed by projecting the empirical distribution onto the set of laws satisfying the null. Wasserstein projection (WP) performs this project…
A Geometric Unification of Distributionally Robust Covariance Estimators: Shrinking the Spectrum by Inflating the Ambiguity Set
Man-Chung Yue, Yves Rychener, Daniel Kuhn +1
The state-of-the-art methods for estimating high-dimensional covariance matrices all shrink the eigenvalues of the sample covariance matrix towards a data-insensitive shrinkage tar…
Wasserstein-based Minimax Estimation of Dependence in Multivariate Regularly Varying Extremes
Xuhui Zhang, Jose Blanchet, Youssef Marzouk +2
We present the first minimax risk bounds for estimators of the spectral measure in multivariate linear factor models, where observations are linear combinations of regularly varyin…
Efficient Failure Pattern Identification of Predictive Algorithms
Bao Nguyen, Viet Anh Nguyen
Given a (machine learning) classifier and a collection of unlabeled data, how can we efficiently identify misclassification patterns presented in this dataset? To address this prob…
Dynamic Flows on Curved Space Generated by Labeled Data
Xinru Hua, Truyen Nguyen, Tam Le +2
The scarcity of labeled data is a long-standing challenge for many machine learning tasks. We propose our gradient flow method to leverage the existing dataset (i.e., source) to ge…
Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints
Jiajin Li, Sirui Lin, Jose Blanchet +1
Distributionally robust optimization has been shown to offer a principled way to regularize learning models. In this paper, we find that Tikhonov regularization is distributionally…
Robust Bayesian Recourse
Tuan-Duy H. Nguyen, Ngoc Bui, Duy Nguyen +2
Algorithmic recourse aims to recommend an informative feedback to overturn an unfavorable machine learning decision. We introduce in this paper the Bayesian recourse, a model-agnos…
Distributionally Robust Gaussian Process Regression and Bayesian Inverse Problems
Xuhui Zhang, Jose Blanchet, Youssef Marzouk +2
We study a distributionally robust optimization formulation (i.e., a min-max game) for two representative problems in Bayesian nonparametric estimation: Gaussian process regression…
Sobolev Transport: A Scalable Metric for Probability Measures with Graph Metrics
Tam Le, Truyen Nguyen, Dinh Phung +1
Optimal transport (OT) is a popular measure to compare probability distributions. However, OT suffers a few drawbacks such as (i) a high complexity for computation, (ii) indefinite…
Distributionally Robust Fair Principal Components via Geodesic Descents
Hieu Vu, Toan Tran, Man-Chung Yue +1
Principal component analysis is a simple yet useful dimensionality reduction technique in modern machine learning pipelines. In consequential domains such as college admission, hea…
Bayesian Imputation with Optimal Look-Ahead-Bias and Variance Tradeoff
Jose Blanchet, Fernando Hernandez, Viet Anh Nguyen +2
Missing time-series data is a prevalent problem in many prescriptive analytics models in operations management, healthcare and finance. Imputation methods for time-series data are…
Counterfactual Plans under Distributional Ambiguity
Ngoc Bui, Duy Nguyen, Viet Anh Nguyen
Counterfactual explanations are attracting significant attention due to the flourishing applications of machine learning models in consequential domains. A counterfactual plan cons…
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…