2 citations · 2 across the 7 of their papers we have counts for
7 papers
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…
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…
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…
DeLELSTM: Decomposition-based Linear Explainable LSTM to Capture Instantaneous and Long-term Effects in Time Series
Chaoqun Wang, Yijun Li, Xiangqian Sun +3
Time series forecasting is prevalent in various real-world applications. Despite the promising results of deep learning models in time series forecasting, especially the Recurrent…
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…
Neural Partial Differential Equations with Functional Convolution
Ziqian Wu, Xingzhe He, Yijun Li +4
We present a lightweighted neural PDE representation to discover the hidden structure and predict the solution of different nonlinear PDEs. Our key idea is to leverage the prior of…