Cover Image

Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Chen, George H.
Format:	Preprint
Published:	2022
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Deep Copula-Based Survival Analysis for Dependent Censoring with Identifiability Guarantees
by: Zhang, Weijia, et al.
Published: (2023)

Fairness in Survival Analysis with Distributionally Robust Optimization
by: Hu, Shu, et al.
Published: (2024)

Survival Analysis as Imprecise Classification with Trainable Kernels
by: Konstantinov, Andrei V., et al.
Published: (2025)

ADHAM: Additive Deep Hazard Analysis Mixtures for Interpretable Survival Regression
by: Ketenci, Mert, et al.
Published: (2025)

Interpretable Machine Learning for Survival Analysis
by: Langbein, Sophie Hanna, et al.
Published: (2024)

Interpretable Prediction and Feature Selection for Survival Analysis
by: Van Ness, Mike, et al.
Published: (2024)

Deep Clustering Survival Machines with Interpretable Expert Distributions
by: Hou, Bojian, et al.
Published: (2023)

Deep Kernel Aalen-Johansen Estimator: An Interpretable and Flexible Neural Net Framework for Competing Risks
by: Shen, Xiaobin, et al.
Published: (2025)

Deep Learning for Survival Analysis: A Review
by: Wiegrebe, Simon, et al.
Published: (2023)

SurvReLU: Inherently Interpretable Survival Analysis via Deep ReLU Networks
by: Sun, Xiaotong, et al.
Published: (2024)

Deep Semi-Supervised Survival Analysis for Predicting Cancer Prognosis
by: Sun, Anchen, et al.
Published: (2026)

DNAMite: Interpretable Calibrated Survival Analysis with Discretized Additive Models
by: Van Ness, Mike, et al.
Published: (2024)

Automated and Interpretable Survival Analysis from Multimodal Data
by: Malafaia, Mafalda, et al.
Published: (2025)

PISA: An AI Pipeline for Interpretable-by-design Survival Analysis Providing Multiple Complexity-Accuracy Trade-off Models
by: Schlender, Thalea, et al.
Published: (2025)

Deep End-to-End Survival Analysis with Temporal Consistency
by: Vieyra, Mariana Vargas, et al.
Published: (2024)

Adaptive Sentencing Prediction with Guaranteed Accuracy and Legal Interpretability
by: Jin, Yifei, et al.
Published: (2025)

Spectral Survival Analysis
by: Shi, Chengzhi, et al.
Published: (2025)

Generating Survival Interpretable Trajectories and Data
by: Konstantinov, Andrei V., et al.
Published: (2024)

NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification
by: Monod, Mélodie, et al.
Published: (2025)

TorchSurv: A Lightweight Package for Deep Survival Analysis
by: Monod, Mélodie, et al.
Published: (2024)

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

Interpretable Non-linear Survival Analysis with Evolutionary Symbolic Regression
by: Rovito, Luigi, et al.
Published: (2025)

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

Functional Decomposition and Shapley Interactions for Interpreting Survival Models
by: Langbein, Sophie Hanna, et al.
Published: (2026)

Reduction Techniques for Survival Analysis
by: Piller, Johannes, et al.
Published: (2025)

Survival In-Context: Amortized Bayesian Survival Analysis via Prior-Fitted Networks
by: Seletkov, Dmitrii, et al.
Published: (2026)

Variational Deep Survival Machines: Survival Regression with Censored Outcomes
by: Wang, Qinxin, et al.
Published: (2024)

CoxNTF: A New Approach for Joint Clustering and Prediction in Survival Analysis
by: Fogel, Paul, et al.
Published: (2025)

Isotonic Survival Regression: Calibrated Survival Distributions from Deep Cox Models
by: Jain, Anchit, et al.
Published: (2026)

Survival Reinforcement Learning: Toward Scalable Self-Supervised RL
by: Nguimatsia-Tiofack, Franki, et al.
Published: (2026)

The Impact of Medication Non-adherence on Adverse Outcomes: Evidence from Schizophrenia Patients via Survival Analysis
by: Noroozizadeh, Shahriar, et al.
Published: (2025)

ConSurv: Multimodal Continual Learning for Survival Analysis
by: Yu, Dianzhi, et al.
Published: (2025)

SDPM: Survival Diffusion Probabilistic Model for Continuous-Time Survival Analysis
by: Kirpichenko, Stanislav R., et al.
Published: (2026)

Gradient-based Explanations for Deep Learning Survival Models
by: Langbein, Sophie Hanna, et al.
Published: (2025)

Predicting Breast Cancer Survival: A Survival Analysis Approach Using Log Odds and Clinical Variables
by: Alamu, Opeyemi Sheu, et al.
Published: (2024)

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

Analyzing Breast Cancer Survival Disparities by Race and Demographic Location: A Survival Analysis Approach
by: Farha, Ramisa, et al.
Published: (2025)

Scalable Random Wavelet Features: Efficient Non-Stationary Kernel Approximation with Convergence Guarantees
by: Kumar, Sawan, et al.
Published: (2026)

Dynamic Survival Analysis for Early Event Prediction
by: Yèche, Hugo, et al.
Published: (2024)