4 citations · 6 across the 8 of their papers we have counts for
9 papers
How much Data do We Need? Sequential Data Collection for Stochastic Programming
Xin Li, Juergen Branke, Xuan Vinh Doan
Data-driven optimization often requires collecting data to estimate uncertain model parameters before solving the underlying decision problem. In practice, however, data acquisitio…
Learning from data with structured missingness
Robin Mitra, Sarah F. McGough, Tapabrata Chakraborti +17
Missing data are an unavoidable complication in many machine learning tasks. When data are `missing at random' there exist a range of tools and techniques to deal with the issue. H…
Fairness Criteria for Allocating Indivisible Chores: Connections and Efficiencies
Ankang Sun, Bo Chen, Xuan Vinh Doan
We study several fairness notions in allocating indivisible chores (i.e., items with non-positive values) to agents who have additive and submodular cost functions. The fairness cr…
Low-rank matrix recovery with Ky Fan 2-k-norm
Xuan Vinh Doan, Stephen Vavasis
We propose Ky Fan 2-k-norm-based models for the nonconvex low-rank matrix recovery problem. A general difference of convex algorithm (DCA) is developed to solve these models. Numer…
Robust Newsvendor Games with Ambiguity in Demand Distributions
Xuan Vinh Doan, Tri-Dung Nguyen
We investigate newsvendor games whose payoff function is uncertain due to ambiguity in demand distributions. We discuss the concept of stability under uncertainty and introduce sol…
Finding the largest low-rank clusters with Ky Fan --norm and -norm
Xuan Vinh Doan, Stephen Vavasis
We propose a convex optimization formulation with the Ky Fan --norm and -norm to find largest approximately rank-one submatrix blocks of a given nonnegative matri…