1 citations · 1 across the 6 of their papers we have counts for
6 papers
From Optimization to Satisficing: Robust Screening under Distributional Ambiguity
Shumin Ma, Daniel Zhuoyu Long, Lijian Lu
This study investigates a robust screening problem under distributional ambiguity, where a seller is uncertain about a buyer's true valuation distribution, knowing only that it lie…
Trading Under Uncertainty: A Distribution-Based Strategy for Futures Markets Using FutureQuant Transformer
Wenhao Guo, Yuda Wang, Zeqiao Huang +2
In the complex landscape of traditional futures trading, where vast data and variables like real-time Limit Order Books (LOB) complicate price predictions, we introduce the FutureQ…
Deep into The Domain Shift: Transfer Learning through Dependence Regularization
Shumin Ma, Zhiri Yuan, Qi Wu +5
Classical Domain Adaptation methods acquire transferability by regularizing the overall distributional discrepancies between features in the source domain (labeled) and features in…
Moderately-Balanced Representation Learning for Treatment Effects with Orthogonality Information
Yiyan Huang, Cheuk Hang Leung, Shumin Ma +3
Estimating the average treatment effect (ATE) from observational data is challenging due to selection bias. Existing works mainly tackle this challenge in two ways. Some researcher…
Robust Causal Learning for the Estimation of Average Treatment Effects
Yiyan Huang, Cheuk Hang Leung, Xing Yan +5
Many practical decision-making problems in economics and healthcare seek to estimate the average treatment effect (ATE) from observational data. The Double/Debiased Machine Learnin…
Understanding Distributional Ambiguity via Non-robust Chance Constraint
Qi Wu, Shumin Ma, Cheuk Hang Leung +2
This paper provides a non-robust interpretation of the distributionally robust optimization (DRO) problem by relating the distributional uncertainties to the chance probabilities.…