1 citations · 3 across the 7 of their papers we have counts for
7 papers
PASTA: A Unified Framework for Offline Assortment Learning
Juncheng Dong, Weibin Mo, Zhengling Qi +3
We study a broad class of assortment optimization problems in an offline and data-driven setting. In such problems, a firm lacks prior knowledge of the underlying choice model, and…
Hub Detection in Gaussian Graphical Models
José Á. Sánchez Gómez, Weibin Mo, Junlong Zhao +1
Graphical models are popular tools for exploring relationships among a set of variables. The Gaussian graphical model (GGM) is an important class of graphical models, where the con…
Minimax Regret Learning for Data with Heterogeneous Subgroups
Weibin Mo, Weijing Tang, Songkai Xue +2
Modern complex datasets often consist of various sub-populations with known group information. In the presence of sub-population heterogeneity, it is crucial to develop robust and…
PASTA: Pessimistic Assortment Optimization
Juncheng Dong, Weibin Mo, Zhengling Qi +3
We consider a class of assortment optimization problems in an offline data-driven setting. A firm does not know the underlying customer choice model but has access to an offline da…
Rejoinder: Learning Optimal Distributionally Robust Individualized Treatment Rules
Weibin Mo, Zhengling Qi, Yufeng Liu
We thank the opportunity offered by editors for this discussion and the discussants for their insightful comments and thoughtful contributions. We also want to congratulate Kallus…
Efficient Learning of Optimal Individualized Treatment Rules for Heteroscedastic or Misspecified Treatment-Free Effect Models
Weibin Mo, Yufeng Liu
Recent development in data-driven decision science has seen great advances in individualized decision making. Given data with individual covariates, treatment assignments and outco…