3 citations · 3 across the 3 of their papers we have counts for
4 papers
Learning Deployable Causal Action Geometry under Temporal Non-Stationarity
Changjian Liu, Tianyu Wang, Yuwei Xu +6
Learning a response law for continuous decisions from observational time series is central to many real-world decision systems, yet temporal non-stationarity poses a fundamental ch…
Multi-channel Uplift Policy Learning
Changjian Liu, Tianyu Wang, Xiaoxuan Deng +7
E-commerce platforms must allocate fixed marketing budgets across multiple channels to maximize business utility. However, standard predict-then-optimize (PTO) paradigms fail in th…
OpFlow: Learning Opportunity-Conditioned Choice Potentials for Robust OD Flow Prediction
Changjian Liu, Yong Gao, Yuqing Wang +5
Origin-destination (OD) flow prediction is central to urban analytics, yet deep models trained on raw counts remain vulnerable to distribution shift. The core problem is that raw c…
GWRBoost:A geographically weighted gradient boosting method for explainable quantification of spatially-varying relationships
Han Wang, Zhou Huang, Ganmin Yin +3
The geographically weighted regression (GWR) is an essential tool for estimating the spatial variation of relationships between dependent and independent variables in geographical…