1 citations · 2 across the 6 of their papers we have counts for
6 papers
Distributionally Fair Stochastic Optimization using Wasserstein Distance
Qing Ye, Grani A. Hanasusanto, Weijun Xie
A traditional stochastic program under a finite population typically seeks to optimize efficiency by maximizing the expected profits or minimizing the expected costs, subject to a…
On Tractability, Complexity, and Mixed-Integer Convex Programming Representability of Distributionally Favorable Optimization
Nan Jiang, Weijun Xie
Distributionally Favorable Optimization (DFO) is an important framework for decision-making under uncertainty, with applications across fields such as reinforcement learning, onlin…
On Sparse Canonical Correlation Analysis
Yongchun Li, Santanu S. Dey, Weijun Xie
The classical Canonical Correlation Analysis (CCA) identifies the correlations between two sets of multivariate variables based on their covariance, which has been widely applied i…
ALSO-X#: Better Convex Approximations for Distributionally Robust Chance Constrained Programs
Nan Jiang, Weijun Xie
This paper studies distributionally robust chance constrained programs (DRCCPs), where the uncertain constraints must be satisfied with at least a probability of a prespecified thr…
A note on quadratic constraints with indicator variables: Convex hull description and perspective relaxation
Andres Gomez, Weijun Xie
In this paper, we study the mixed-integer nonlinear set given by a separable quadratic constraint on continuous variables, where each continuous variable is controlled by an additi…
D-optimal Data Fusion: Exact and Approximation Algorithms
Yongchun Li, Marcia Fampa, Jon Lee +3
We study the D-optimal Data Fusion (DDF) problem, which aims to select new data points, given an existing Fisher information matrix, so as to maximize the logarithm of the determin…