20 citations · 46 across the 16 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…
Distributionally Robust Local Non-parametric Conditional Estimation
Viet Anh Nguyen, Fan Zhang, Jose Blanchet +2
Conditional estimation given specific covariate values (i.e., local conditional estimation or functional estimation) is ubiquitously useful with applications in engineering, social…
Distributionally Robust Parametric Maximum Likelihood Estimation
Viet Anh Nguyen, Xuhui Zhang, Jose Blanchet +1
We consider the parameter estimation problem of a probabilistic generative model prescribed using a natural exponential family of distributions. For this problem, the typical maxim…