Image de couverture de livre

Graph Neural Network with Two Uplift Estimators for Label-Scarcity Individual Uplift Modeling

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Zhu, Dingyuan, Wang, Daixin, Zhang, Zhiqiang, Kuang, Kun, Zhang, Yan, Kang, Yulin, Zhou, Jun
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Financial Default Prediction via Motif-preserving Graph Neural Network with Curriculum Learning
par: Wang, Daixin, et autres
Publié: (2024)

A New Transformation Approach for Uplift Modeling with Binary Outcome
par: Li, Kun, et autres
Publié: (2023)

Uplift Modeling Under Limited Supervision
par: Panagopoulos, George, et autres
Publié: (2024)

Direct Profit Estimation Using Uplift Modeling under Clustered Network Interference
par: Akker, Bram van den
Publié: (2025)

TSCAN: Context-Aware Uplift Modeling via Two-Stage Training for Online Merchant Business Diagnosis
par: Zhang, Hangtao, et autres
Publié: (2025)

A Comparative Study of Model Adaptation Strategies for Multi-Treatment Uplift Modeling
par: Zhang, Ruyue, et autres
Publié: (2025)

Benchmarking for Deep Uplift Modeling in Online Marketing
par: Liu, Dugang, et autres
Publié: (2024)

Optimizing Long-tailed Link Prediction in Graph Neural Networks through Structure Representation Enhancement
par: Wang, Yakun, et autres
Publié: (2024)

UTBoost: Gradient Boosted Decision Trees for Uplift Modeling
par: Gao, Junjie, et autres
Publié: (2023)

Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization
par: Sun, Zexu, et autres
Publié: (2023)

Hierarchical Contextual Uplift Bandits for Catalog Personalization
par: Agrawal, Anupam, et autres
Publié: (2026)

Rankability-enhanced Revenue Uplift Modeling Framework for Online Marketing
par: He, Bowei, et autres
Publié: (2024)

FairUDT: Fairness-aware Uplift Decision Trees
par: Zahid, Anam, et autres
Publié: (2025)

Orthogonal Uplift Learning with Permutation-Invariant Representations for Combinatorial Treatments
par: Su, Xinyan, et autres
Publié: (2026)

Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent Marketing
par: Huang, Yinqiu, et autres
Publié: (2024)

Fairness Evaluation for Uplift Modeling in the Absence of Ground Truth
par: Kadioglu, Serdar, et autres
Publié: (2024)

Robust Uplift Modeling with Large-Scale Contexts for Real-time Marketing
par: Sun, Zexu, et autres
Publié: (2025)

Uplift modeling with continuous treatments: A predict-then-optimize approach
par: De Vos, Simon, et autres
Publié: (2024)

MOYU: A Theoretical Study on Massive Over-activation Yielded Uplifts in LLMs
par: Ma, Chi, et autres
Publié: (2024)

Evaluating Uplift Modeling under Structural Biases: Insights into Metric Stability and Model Robustness
par: Yang, Yuxuan, et autres
Publié: (2026)

Budget-Constrained Causal Bandits: Bridging Uplift Modeling and Sequential Decision-Making
par: Pillai, Abhirami
Publié: (2026)

We Have It Covered: A Resampling-based Method for Uplift Model Comparison
par: Liu, Yang, et autres
Publié: (2025)

Which Company Adjustment Matter? Insights from Uplift Modeling on Financial Health
par: Wang, Xinlin, et autres
Publié: (2025)

Multi-Treatment Multi-Task Uplift Modeling for Enhancing User Growth
par: Wei, Yuxiang, et autres
Publié: (2024)

Guardrailed Uplift Targeting: A Causal Optimization Playbook for Marketing Strategy
par: Sapru, Deepit
Publié: (2025)

Jointly Optimizing Debiased CTR and Uplift for Coupons Marketing: A Unified Causal Framework
par: Yang, Siyun, et autres
Publié: (2026)

End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift Modeling
par: Sun, Zexu, et autres
Publié: (2024)

VALOR: Value-Aware Revenue Uplift Modeling with Treatment-Gated Representation for B2B Sales
par: Guduguntla, Vamshi, et autres
Publié: (2026)

Personalized Binomial DAGs Learning with Network Structured Covariates
par: Zhao, Boxin, et autres
Publié: (2024)

M$^3$TN: Multi-gate Mixture-of-Experts based Multi-valued Treatment Network for Uplift Modeling
par: Sun, Zexu, et autres
Publié: (2024)

Enhancing Uplift Modeling in Multi-Treatment Marketing Campaigns: Leveraging Score Ranking and Calibration Techniques
par: Park, Yoon Tae, et autres
Publié: (2024)

A Large-Scale Empirical Comparison of Meta-Learners and Causal Forests for Heterogeneous Treatment Effect Estimation in Marketing Uplift Modeling
par: Singh, Aman
Publié: (2026)

When Sharpening Becomes Collapse: Sampling Bias and Semantic Coupling in RL with Verifiable Rewards
par: Fan, Mingyuan, et autres
Publié: (2026)

Unifying Adversarial Perturbation for Graph Neural Networks
par: Yang, Jinluan, et autres
Publié: (2025)

Mix Data or Merge Models? Balancing the Helpfulness, Honesty, and Harmlessness of Large Language Model via Model Merging
par: Yang, Jinluan, et autres
Publié: (2025)

When Noisy Labels Meet Class Imbalance on Graphs: A Graph Augmentation Method with LLM and Pseudo Label
par: Xia, Riting, et autres
Publié: (2025)

Smoothness Really Matters: A Simple Yet Effective Approach for Unsupervised Graph Domain Adaptation
par: Chen, Wei, et autres
Publié: (2024)

Improving Graph Neural Networks on Multi-node Tasks with the Labeling Trick
par: Wang, Xiyuan, et autres
Publié: (2023)

Understanding When Graph Convolutional Networks Help: A Diagnostic Study on Label Scarcity and Structural Properties
par: Subedi, Nischal, et autres
Publié: (2025)

IceBerg: Debiased Self-Training for Class-Imbalanced Node Classification
par: Li, Zhixun, et autres
Publié: (2025)