Imagen de Portada

A Trainable Centrality Framework for Modern Data

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Vu, Minh Duc, Liu, Mingshuo, Zhou, Doudou
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Methodology
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Generalized Independence Test for Modern Data
por: Liu, Mingshuo, et al.
Publicado: (2024)

WISE: A Weighted Similarity Aggregation Test for Serial Independence
por: Zhu, Qihua, et al.
Publicado: (2025)

Latent Factor Point Processes for Patient Representation in Electronic Health Records
por: Knight, Parker, et al.
Publicado: (2025)

Inference of Dependency Knowledge Graph for Electronic Health Records
por: Xu, Zhiwei, et al.
Publicado: (2023)

Federated Offline Reinforcement Learning
por: Zhou, Doudou, et al.
Publicado: (2022)

Asymptotic Distribution-free Change-point Detection for Modern Data Based on a New Ranking Scheme
por: Zhou, Doudou, et al.
Publicado: (2022)

UTOPIA: Universally Trainable Optimal Prediction Intervals Aggregation
por: Fan, Jianqing, et al.
Publicado: (2023)

Learning Sequential Decisions from Multiple Sources via Group-Robust Markov Decision Processes
por: Xu, Mingyuan, et al.
Publicado: (2026)

A Relative Ignorability Framework for Decision-Relevant Observability in Control Theory and Reinforcement Learning
por: Bleile, MaryLena, et al.
Publicado: (2025)

Preference-based Centrality and Ranking in General Metric Spaces
por: Lyu, Lingfeng, et al.
Publicado: (2026)

Classifier Pooling for Modern Ordinal Classification
por: Rotenberg, Noam H., et al.
Publicado: (2026)

Cross-Validated Causal Inference: a Modern Method to Combine Experimental and Observational Data
por: Yang, Xuelin, et al.
Publicado: (2025)

Two-Stage Data Synthesization: A Statistics-Driven Restricted Trade-off between Privacy and Prediction
por: Liu, Xiaotong, et al.
Publicado: (2026)

Minimum Specification Perturbation: Robustness as Distance-to-Falsification in Causal Inference
por: Dang, Hoang, et al.
Publicado: (2026)

A Black-Box Debiasing Framework for Conditional Sampling
por: Cui, Han, et al.
Publicado: (2025)

Conformal Prediction Assessment: A Framework for Conditional Coverage Evaluation and Selection
por: Zhou, Zheng, et al.
Publicado: (2026)

Causal Discovery on Dependent Binary Data
por: Chen, Alex, et al.
Publicado: (2024)

A Causal Inference Framework for Data Rich Environments
por: Abadie, Alberto, et al.
Publicado: (2025)

The Bayesian Reflex: Online Learning as the Autonomic Nervous System of Modern and Future AI
por: Bhattacharya, Durba, et al.
Publicado: (2026)

A Unified Hierarchical Multi-Task Multi-Fidelity Framework for Data-Efficient Surrogate Modeling in Manufacturing
por: Mehta, Manan, et al.
Publicado: (2026)

GLACIAL: Granger and Learning-based Causality Analysis for Longitudinal Imaging Studies
por: Nguyen, Minh, et al.
Publicado: (2022)

Efficient Covariance Estimation for Sparsified Functional Data
por: Zheng, Sijie, et al.
Publicado: (2025)

Representation-Aware Distributionally Robust Optimization: A Knowledge Transfer Framework
por: Wang, Zitao, et al.
Publicado: (2025)

Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions
por: Deliu, Nina, et al.
Publicado: (2022)

Score Matching With Missing Data
por: Givens, Josh, et al.
Publicado: (2025)

A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation
por: Song, Xinran, et al.
Publicado: (2025)

Scalable Counterfactual Risk Estimation for Rare Events in Longitudinal Data
por: Yin, Xiaohui, et al.
Publicado: (2026)

Semiparametric Learning from Open-Set Label Shift Data
por: Liu, Siyan, et al.
Publicado: (2025)

Data Fusion with Distributional Equivalence Test-then-pool
por: Yang, Linying, et al.
Publicado: (2026)

Spectrum-Aware Debiasing: A Modern Inference Framework with Applications to Principal Components Regression
por: Li, Yufan, et al.
Publicado: (2023)

M-learner:A Flexible And Powerful Framework To Study Heterogeneous Treatment Effect In Mediation Model
por: Li, Xingyu, et al.
Publicado: (2025)

Bures-Wasserstein Importance-Weighted Evidence Lower Bound: Exposition and Applications
por: Jiang, Peiwen, et al.
Publicado: (2026)

Inversion-Free Natural Gradient Descent on Riemannian Manifolds
por: Draca, Dario, et al.
Publicado: (2026)

BaGGLS: A Bayesian Shrinkage Framework for Interpretable Modeling of Interactions in High-Dimensional Biological Data
por: Lemanczyk, Marta S., et al.
Publicado: (2025)

Central limit theorems for the outputs of fully convolutional neural networks with time series input
por: Betken, Annika, et al.
Publicado: (2026)

TabMixNN: A Unified Deep Learning Framework for Structural Mixed Effects Modeling on Tabular Data
por: Akdemir, Deniz
Publicado: (2025)

Positive and Unlabeled Data: Model, Estimation, Inference, and Classification
por: Liu, Siyan, et al.
Publicado: (2024)

A General Framework for Joint Multi-State Models
por: Laplante, Félix, et al.
Publicado: (2025)

A Latent Causal Inference Framework for Ordinal Variables
por: Scauda, Martina, et al.
Publicado: (2025)

A General Causal Inference Framework for Cross-Sectional Observational Data
por: Zhao, Yonghe, et al.
Publicado: (2024)