1 citations · 3 across the 7 of their papers we have counts for
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UTBoost: Gradient Boosted Decision Trees for Uplift Modeling
Junjie Gao, Xiangyu Zheng, DongDong Wang +3
Uplift modeling comprises a collection of machine learning techniques designed for managers to predict the incremental impact of specific actions on customer outcomes. However, acc…
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…
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…