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Found 3 papers, 1 papers with code
M$^3$TN: Multi-gate Mixture-of-Experts based Multi-valued Treatment Network for Uplift Modeling
M$^3$TN consists of two components: 1) a feature representation module with Multi-gate Mixture-of-Experts to improve the efficiency; 2) a reparameterization module by modeling uplift explicitly to improve the effectiveness.
Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization
Specifically, our RUAD can more effectively alleviate the feature sensitivity of the uplift model through two customized modules, including a feature selection module with joint multi-label modeling to identify a key subset from the input features and an adversarial feature desensitization module using adversarial training and soft interpolation operations to enhance the robustness of the model against this selected subset of features.
Offline Imitation Learning with Variational Counterfactual Reasoning
We theoretically analyze the influence of the generated expert data and the improvement of generalization.
