Image de couverture de livre

Learning Optimal Distributionally Robust Individualized Treatment Rules Integrating Multi-Source Data

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Cui, Wenhai, Su, Wen, Zhao, Xingqiu
Format:	Preprint
Publié:	2026
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

Learning Optimal Individualized Decision Rules with Conditional Demographic Parity
par: Cui, Wenhai, et autres
Publié: (2026)

Optimal Transport Learning: Balancing Value Optimization and Fairness in Individualized Treatment Rules
par: Cui, Wenhai, et autres
Publié: (2025)

Demographic Parity-aware Individualized Treatment Rules
par: Cui, Wenhai, et autres
Publié: (2025)

Learning Robust Treatment Rules for Censored Data
par: Cui, Yifan, et autres
Publié: (2024)

Transfer Learning for Classification under Decision Rule Drift with Application to Optimal Individualized Treatment Rule Estimation
par: Wang, Xiaohan, et autres
Publié: (2025)

Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules
par: Li, Michael Lingzhi, et autres
Publié: (2024)

Fusing Individualized Treatment Rules Using Secondary Outcomes
par: Gao, Daiqi, et autres
Publié: (2024)

StablePCA: Distributionally Robust Learning of Shared Representations from Multi-Source Data
par: Wang, Zhenyu, et autres
Publié: (2025)

Optimal Multi-Distribution Learning
par: Zhang, Zihan, et autres
Publié: (2023)

Nonconvex Penalized LAD Estimation in Partial Linear Models with DNNs: Asymptotic Analysis and Proximal Algorithms
par: Feng, Lechen, et autres
Publié: (2025)

Reinforcement Learning for Individual Optimal Policy from Heterogeneous Data
par: Miao, Rui, et autres
Publié: (2025)

Distributionally Robust Policy Evaluation and Learning for Continuous Treatment with Observational Data
par: Leung, Cheuk Hang, et autres
Publié: (2025)

Maximin Learning of Individualized Treatment Effect on Multi-Domain Outcomes
par: Lu, Yuying, et autres
Publié: (2026)

Locally Interpretable Individualized Treatment Rules for Black-Box Decision Models
par: Charvadeh, Yasin Khadem, et autres
Publié: (2026)

Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm
par: Lu, Miao, et autres
Publié: (2024)

Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach
par: He, Xingqiu, et autres
Publié: (2023)

ORVIT: Near-Optimal Online Distributionally Robust Reinforcement Learning
par: Ghosh, Debamita, et autres
Publié: (2025)

Differentially Private Two-Stage Empirical Risk Minimization with Applications to Individualized Treatment Rule
par: Lee, Joowon, et autres
Publié: (2026)

Safe RuleFit: Learning Optimal Sparse Rule Model by Meta Safe Screening
par: Kato, Hiroki, et autres
Publié: (2018)

Minimax-Optimal Multi-Agent Robust Reinforcement Learning
par: Jiao, Yuchen, et autres
Publié: (2024)

Estimation of Optimal Dynamic Treatment Assignment Rules under Policy Constraints
par: Sakaguchi, Shosei
Publié: (2021)

Robust Transfer Learning with Unreliable Source Data
par: Fan, Jianqing, et autres
Publié: (2023)

Robust and Agnostic Learning of Conditional Distributional Treatment Effects
par: Kallus, Nathan, et autres
Publié: (2022)

MixMax: Distributional Robustness in Function Space via Optimal Data Mixtures
par: Thudi, Anvith, et autres
Publié: (2024)

Generalizing Few Data to Unseen Domains Flexibly Based on Label Smoothing Integrated with Distributionally Robust Optimization
par: Wang, Yangdi, et autres
Publié: (2024)

Learning Optimal Distributionally Robust Stochastic Control in Continuous State Spaces
par: Wang, Shengbo, et autres
Publié: (2024)

Learning Optimal Classification Trees Robust to Distribution Shifts
par: Justin, Nathan, et autres
Publié: (2023)

Distributionally Robust Federated Learning: An ADMM Algorithm
par: Bai, Wen, et autres
Publié: (2025)

PITE: Multi-Prototype Alignment for Individual Treatment Effect Estimation
par: Cao, Fuyuan, et autres
Publié: (2025)

Robust Offline Policy Learning with Observational Data from Multiple Sources
par: Carranza, Aldo Gael, et autres
Publié: (2024)

RuleFuser: An Evidential Bayes Approach for Rule Injection in Imitation Learned Planners and Predictors for Robustness under Distribution Shifts
par: Patrikar, Jay, et autres
Publié: (2024)

Optimal Complexity in Byzantine-Robust Distributed Stochastic Optimization with Data Heterogeneity
par: Shi, Qiankun, et autres
Publié: (2025)

3D-Learning: Diffusion-Augmented Distributionally Robust Decision-Focused Learning
par: Wen, Jiaqi, et autres
Publié: (2026)

A Quadratic Synchronization Rule for Distributed Deep Learning
par: Gu, Xinran, et autres
Publié: (2023)

Simulation-Based Sensitivity Analysis in Optimal Treatment Regimes and Causal Decomposition with Individualized Interventions
par: Park, Soojin, et autres
Publié: (2025)

CURLS: Causal Rule Learning for Subgroups with Significant Treatment Effect
par: Zhou, Jiehui, et autres
Publié: (2024)

Near-Optimal Sample Complexities of Divergence-based S-rectangular Distributionally Robust Reinforcement Learning
par: Li, Zhenghao, et autres
Publié: (2025)

Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity
par: Shi, Laixi, et autres
Publié: (2022)

Understanding the Robustness of Multi-modal Contrastive Learning to Distribution Shift
par: Xue, Yihao, et autres
Publié: (2023)

Optimal Data Splitting in Distributed Optimization for Machine Learning
par: Medyakov, Daniil, et autres
Publié: (2024)