2 citations · 3 across the 12 of their papers we have counts for
14 papers
Wasserstein Policy Learning for Distributional Outcomes
Yiyan Huang, Cheuk Hang Leung, Qi Wu +1
Offline policy learning has received growing attention in causal inference. The primary objective is to learn a policy (individualized treatment rule) as a mapping from covariates…
Distribution-valued Causal Machine Learning: Implications of Credit on Spending Patterns
Cheuk Hang Leung, Yijun Li, Qi Wu
Fintech lending has become a central mechanism through which digital platforms stimulate consumption, offering dynamic, personalized credit limits that directly shape the purchasin…
Distributionally Robust Policy Evaluation and Learning for Continuous Treatment with Observational Data
Cheuk Hang Leung, Yiyan Huang, Yijun Li +1
Using offline observational data for policy evaluation and learning allows decision-makers to evaluate and learn a policy that connects characteristics and interventions. Most exis…
Risk-Neutral Generative Networks
Zhonghao Xian, Xing Yan, Cheuk Hang Leung +1
We present a generative approach to price options and extract risk-neutral densities from the market. Specifically, we model the underlying log-returns on the time-to-maturity cont…
Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators
Yiyan Huang, Cheuk Hang Leung, Siyi Wang +2
The growing demand for personalized decision-making has led to a surge of interest in estimating the Conditional Average Treatment Effect (CATE). Various types of CATE estimators h…
SpecFormer: Guarding Vision Transformer Robustness via Maximum Singular Value Penalization
Xixu Hu, Runkai Zheng, Jindong Wang +3
Vision Transformers (ViTs) are increasingly used in computer vision due to their high performance, but their vulnerability to adversarial attacks is a concern. Existing methods lac…
The Causal Impact of Credit Lines on Spending Distributions
Yijun Li, Cheuk Hang Leung, Xiangqian Sun +6
Consumer credit services offered by e-commerce platforms provide customers with convenient loan access during shopping and have the potential to stimulate sales. To understand the…
Probabilistic Learning of Multivariate Time Series with Temporal Irregularity
Yijun Li, Cheuk Hang Leung, Qi Wu
Probabilistic forecasting of multivariate time series is essential for various downstream tasks. Most existing approaches rely on the sequences being uniformly spaced and aligned a…
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…
Robust Orthogonal Machine Learning of Treatment Effects
Yiyan Huang, Cheuk Hang Leung, Qi Wu +1
Causal learning is the key to obtaining stable predictions and answering \textit{what if} problems in decision-makings. In causal learning, it is central to seek methods to estimat…
The Causal Learning of Retail Delinquency
Yiyan Huang, Cheuk Hang Leung, Xing Yan +4
This paper focuses on the expected difference in borrower's repayment when there is a change in the lender's credit decisions. Classical estimators overlook the confounding effects…
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.…