Imagen de Portada

Deep Kernel Aalen-Johansen Estimator: An Interpretable and Flexible Neural Net Framework for Competing Risks

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Shen, Xiaobin, Chen, George H.
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee
por: Chen, George H.
Publicado: (2022)

Conditional Aalen–Johansen estimation
por: Martin Bladt, et al.
Publicado: (2025)

Conditional Aalen--Johansen estimation
por: Bladt, Martin, et al.
Publicado: (2023)

Stepwise Fine and Gray: Subject-Specific Variable Selection Shows When Hemodynamic Data Improves Prognostication of Comatose Post-Cardiac Arrest Patients
por: Shen, Xiaobin, et al.
Publicado: (2025)

CRISP-NAM: Competing Risks Interpretable Survival Prediction with Neural Additive Models
por: Ramachandram, Dhanesh, et al.
Publicado: (2025)

SurvHTE-Bench: A Benchmark for Heterogeneous Treatment Effect Estimation in Survival Analysis
por: Noroozizadeh, Shahriar, et al.
Publicado: (2026)

Competing Risks: Impact on Risk Estimation and Algorithmic Fairness
por: Jeanselme, Vincent, et al.
Publicado: (2025)

Individual claims reserving using the Aalen--Johansen estimator
por: Bladt, Martin, et al.
Publicado: (2023)

DNA-SE: Towards Deep Neural-Nets Assisted Semiparametric Estimation
por: Liu, Qinshuo, et al.
Publicado: (2024)

Learning Interpretable Characteristic Kernels via Decision Forests
por: Panda, Sambit, et al.
Publicado: (2018)

Interpretable Kernels
por: Groenen, Patrick J. F., et al.
Publicado: (2025)

The Loss Kernel: A Geometric Probe for Deep Learning Interpretability
por: Adam, Maxwell, et al.
Publicado: (2025)

Deep Learning of Semi-Competing Risk Data via a New Neural Expectation-Maximization Algorithm
por: Salerno, Stephen, et al.
Publicado: (2022)

SortedNet: A Scalable and Generalized Framework for Training Modular Deep Neural Networks
por: Valipour, Mojtaba, et al.
Publicado: (2023)

Flexible Deep Neural Networks for Partially Linear Survival Data: Estimation and Survival Inference
por: Arie, Asaf Ben, et al.
Publicado: (2025)

Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference
por: Huang, Xunpeng, et al.
Publicado: (2024)

LassoFlexNet: Flexible Neural Architecture for Tabular Data
por: Lui, Kry Yik Chau, et al.
Publicado: (2026)

Uncertainty Estimation by Flexible Evidential Deep Learning
por: Yoon, Taeseong, et al.
Publicado: (2025)

Chordal Sparsity for Lipschitz Constant Estimation of Deep Neural Networks
por: Xue, Anton, et al.
Publicado: (2022)

Neural-g: A Deep Learning Framework for Mixing Density Estimation
por: Wang, Shijie, et al.
Publicado: (2024)

Comparing Normalizing Flows with Kernel Density Estimation in Estimating Risk of Automated Driving Systems
por: de Gelder, Erwin, et al.
Publicado: (2025)

HyCARD-Net: A Synergistic Hybrid Intelligence Framework for Cardiovascular Disease Diagnosis
por: Gupta, Rajan Das, et al.
Publicado: (2026)

Interpreting Deep Neural Networks with the Package innsight
por: Koenen, Niklas, et al.
Publicado: (2023)

Efficient and Flexible Method for Reducing Moderate-size Deep Neural Networks with Condensation
por: Chen, Tianyi, et al.
Publicado: (2024)

F\textsuperscript{2}LP-AP: Fast \& Flexible Label Propagation with Adaptive Propagation Kernel
por: Shen, Yutong, et al.
Publicado: (2026)

Depth-induced NTK: Bridging Over-parameterized Neural Networks and Deep Neural Kernels
por: Tian, Yong-Ming, et al.
Publicado: (2025)

HYDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks
por: Chen, Yuanyuan, et al.
Publicado: (2021)

Deep Survival Analysis for Competing Risk Modeling with Functional Covariates and Missing Data Imputation
por: Gao, Penglei, et al.
Publicado: (2025)

Fast and Interpretable Autoregressive Estimation with Neural Network Backpropagation
por: Lucena, Anaísa, et al.
Publicado: (2026)

Kernel-U-Net: Multivariate Time Series Forecasting using Custom Kernels
por: You, Jiang, et al.
Publicado: (2024)

An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event Outcomes
por: Chen, George H.
Publicado: (2024)

Learning An Interpretable Risk Scoring System for Maximizing Decision Net Benefit
por: Chi, Wenhao, et al.
Publicado: (2026)

"Lossless" Compression of Deep Neural Networks: A High-dimensional Neural Tangent Kernel Approach
por: Gu, Lingyu, et al.
Publicado: (2024)

Towards Interpretable Deep Neural Networks for Tabular Data
por: Elhadri, Khawla, et al.
Publicado: (2025)

Correlating Time Series with Interpretable Convolutional Kernels
por: Chen, Xinyu, et al.
Publicado: (2024)

Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers
por: Súkeník, Peter, et al.
Publicado: (2025)

SnareNet: Flexible Repair Layers for Neural Networks with Hard Constraints
por: Chu, Ya-Chi, et al.
Publicado: (2026)

Deep Survival Analysis in Multimodal Medical Data: A Parametric and Probabilistic Approach with Competing Risks
por: Garrido, Alba, et al.
Publicado: (2025)

SpectralNet: Spectral Clustering using Deep Neural Networks
por: Shaham, Uri, et al.
Publicado: (2018)

How Interpretable Are Interpretable Graph Neural Networks?
por: Chen, Yongqiang, et al.
Publicado: (2024)