Buchumschlag

Explainable Federated Bayesian Causal Inference and Its Application in Advanced Manufacturing

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Xiao, Xiaofeng, Alharbi, Khawlah, Zhang, Pengyu, Qin, Hantang, Yue, Xubo
Format:	Preprint
Veröffentlicht:	2025
Schlagworte:	Machine Learning Applications
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Advancing Causal Inference: A Nonparametric Approach to ATE and CATE Estimation with Continuous Treatments
von: Souto, Hugo Gobato, et al.
Veröffentlicht: (2024)

Bayesian Federated Inference for estimating Statistical Models based on Non-shared Multicenter Data sets
von: Jonker, Marianne A., et al.
Veröffentlicht: (2023)

Testing Generalizability in Causal Inference
von: Manela, Daniel de Vassimon, et al.
Veröffentlicht: (2024)

Bayesian Inference on Brain-Computer Interfaces via GLASS
von: Zhao, Bangyao, et al.
Veröffentlicht: (2023)

Causal Inference with Double/Debiased Machine Learning for Evaluating the Health Effects of Multiple Mismeasured Pollutants
von: Xu, Gang, et al.
Veröffentlicht: (2024)

Causal Inference for Genomic Data with Multiple Heterogeneous Outcomes
von: Du, Jin-Hong, et al.
Veröffentlicht: (2024)

Bayesian Nonlinear PDE Inference via Gaussian Process Collocation with Application to the Richards Equation
von: Yang, Yumo, et al.
Veröffentlicht: (2025)

Combining Bayesian and Frequentist Inference for Laboratory-Specific Performance Guarantees in Copy Number Variation Detection
von: Talbot, Austin, et al.
Veröffentlicht: (2026)

SLEM: Machine Learning for Path Modeling and Causal Inference with Super Learner Equation Modeling
von: Vowels, Matthew J.
Veröffentlicht: (2023)

Sample Efficient Bayesian Learning of Causal Graphs from Interventions
von: Zhou, Zihan, et al.
Veröffentlicht: (2024)

Implicit Generative Prior for Bayesian Neural Networks
von: Liu, Yijia, et al.
Veröffentlicht: (2024)

Bayesian Federated Inference for regression models based on non-shared multicenter data sets from heterogeneous populations
von: Jonker, Marianne A, et al.
Veröffentlicht: (2024)

A Bayesian Framework for Causal Analysis of Recurrent Events with Timing Misalignment
von: Oganisian, Arman, et al.
Veröffentlicht: (2023)

Testing and Improving the Robustness of Amortized Bayesian Inference for Cognitive Models
von: Wu, Yufei, et al.
Veröffentlicht: (2024)

Bayesian Causal Forests for Longitudinal Data: Assessing the Impact of Part-Time Work on Growth in High School Mathematics Achievement
von: McJames, Nathan, et al.
Veröffentlicht: (2024)

Fast Uncertainty Quantification for Kernel-Based Estimators in Large-Scale Causal Inference
von: Kosko, Matthew, et al.
Veröffentlicht: (2026)

Scalable Inference for Bayesian Multinomial Logistic-Normal Dynamic Linear Models
von: Saxena, Manan, et al.
Veröffentlicht: (2024)

Spatially-Heterogeneous Causal Bayesian Networks for Seismic Multi-Hazard Estimation: A Variational Approach with Gaussian Processes and Normalizing Flows
von: Li, Xuechun, et al.
Veröffentlicht: (2025)

Bayesian Federated Cause-of-Death Classification and Quantification Under Distribution Shift
von: Zhu, Yu, et al.
Veröffentlicht: (2025)

Constrained Bayesian Optimization under Bivariate Gaussian Process with Application to Cure Process Optimization
von: Li, Yezhuo, et al.
Veröffentlicht: (2025)

CausalGDP: Causality-Guided Diffusion Policies for Reinforcement Learning
von: Xiao, Xiaofeng, et al.
Veröffentlicht: (2026)

Hybrid$^2$ Neural ODE Causal Modeling and an Application to Glycemic Response
von: Zou, Bob Junyi, et al.
Veröffentlicht: (2024)

MMformer with Adaptive Transferable Attention: Advancing Multivariate Time Series Forecasting for Environmental Applications
von: Xin, Ning, et al.
Veröffentlicht: (2025)

Bayesian Safe Policy Learning with Chance Constrained Optimization: Application to Military Security Assessment during the Vietnam War
von: Jia, Zeyang, et al.
Veröffentlicht: (2023)

On the Role of Surrogates in Conformal Inference of Individual Causal Effects
von: Gao, Chenyin, et al.
Veröffentlicht: (2024)

Utilizing Model Residuals to Identify Rental Properties of Interest: The Price Anomaly Score (PAS) and Its Application to Real-time Data in Manhattan
von: Sultan, Youssef, et al.
Veröffentlicht: (2023)

Efficient Causal Structure Learning via Modular Subgraph Integration
von: Sun, Haixiang, et al.
Veröffentlicht: (2026)

A Causal Machine Learning Framework for Treatment Personalization in Clinical Trials: Application to Ulcerative Colitis
von: Minoccheri, Cristian, et al.
Veröffentlicht: (2026)

Estimation and Inference for Causal Explainability
von: Zhang, Weihan, et al.
Veröffentlicht: (2025)

An Explainable AI Model for Predicting the Recurrence of Differentiated Thyroid Cancer
von: Ahmad, Mohammad Al-Sayed, et al.
Veröffentlicht: (2024)

Explainable Boosting Machine for Predicting Claim Severity and Frequency in Car Insurance
von: Krùpovà, Markéta, et al.
Veröffentlicht: (2025)

Hierarchical Sparse Bayesian Multitask Model with Scalable Inference for Microbiome Analysis
von: Zhu, Haonan, et al.
Veröffentlicht: (2025)

A Large Scale Heterogeneous Treatment Effect Estimation Framework and Its Applications of Users' Journey at Snap
von: Pan, Jing, et al.
Veröffentlicht: (2025)

Causal Representation Learning with Generative Artificial Intelligence: Application to Texts as Treatments
von: Imai, Kosuke, et al.
Veröffentlicht: (2024)

Singular Bayesian Neural Networks
von: Toure, Mame Diarra, et al.
Veröffentlicht: (2026)

Efficient Causal Discovery for Autoregressive Time Series
von: Fesanghary, Mohammad, et al.
Veröffentlicht: (2025)

Adaptive Online Experimental Design for Causal Discovery
von: Elahi, Muhammad Qasim, et al.
Veröffentlicht: (2024)

Causal machine learning for predicting treatment outcomes
von: Feuerriegel, Stefan, et al.
Veröffentlicht: (2024)

Piecewise Deterministic Markov Processes for Bayesian Inference of PDE Coefficients
von: Riccius, Leon, et al.
Veröffentlicht: (2026)

Pure Component Property Estimation Framework Using Explainable Machine Learning Methods
von: Jiao, Jianfeng, et al.
Veröffentlicht: (2025)