Cover Image

Neural interval-censored survival regression with feature selection

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Meixide, Carlos García, Matabuena, Marcos, Abraham, Louis, Kosorok, Michael R.
Format:	Preprint
Published:	2022
Subjects:	Machine Learning Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Neural interval‐censored survival regression with feature selection
by: Carlos García Meixide, et al.
Published: (2024)

Uncertainty quantification for intervals
by: Meixide, Carlos García, et al.
Published: (2024)

Distributional Random Forests for Complex Survey Designs on Reproducing Kernel Hilbert Spaces
by: Zou, Yating, et al.
Published: (2025)

Causal survival embeddings: non-parametric counterfactual inference under censoring
by: García-Meixide, Carlos, et al.
Published: (2023)

Variable Selection Methods for Multivariate, Functional, and Complex Biomedical Data in the AI Age
by: Matabuena, Marcos
Published: (2025)

Random-Effects Algorithm for Random Objects in Metric Spaces
by: Matabuena, Marcos, et al.
Published: (2026)

Continuous Temporal Learning of Probability Distributions via Neural ODEs with Applications in Continuous Glucose Monitoring Data
by: Álvarez-López, Antonio, et al.
Published: (2025)

Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spaces
by: Lugosi, Gábor, et al.
Published: (2025)

Screening for Diabetes Mellitus in the U.S. Population Using Neural Network Models and Complex Survey Designs
by: Matabuena, Marcos, et al.
Published: (2024)

A feature selection method based on Shapley values robust to concept shift in regression
by: Sebastián, Carlos, et al.
Published: (2023)

Guiding adaptive shrinkage by co-data to improve regression-based prediction and feature selection
by: van de Wiel, Mark A., et al.
Published: (2024)

High-dimensional Bayesian Tobit regression for censored response with Horseshoe prior
by: Mai, The Tien
Published: (2025)

Medical Knowledge Integration into Reinforcement Learning Algorithms for Dynamic Treatment Regimes
by: Yazzourh, Sophia, et al.
Published: (2024)

A Flexible Framework for Incorporating Patient Preferences Into Q-Learning
by: Zitovsky, Joshua P., et al.
Published: (2023)

ROC Analysis with Covariate Adjustment Using Neural Network Models: Evaluating the Role of Age in the Physical Activity-Mortality Association
by: Hammouri, Ziad Akram Ali, et al.
Published: (2025)

Neural Bayes estimators for censored inference with peaks-over-threshold models
by: Richards, Jordan, et al.
Published: (2023)

Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces
by: Matabuena, Marcos, et al.
Published: (2025)

A regularized multi-state model for covariate selection with interval-censored survival data
by: Bercu, Ariane, et al.
Published: (2025)

Conditional Mean and Variance Estimation via \textit{k}-NN Algorithm with Automated Variance Selection
by: Matabuena, Marcos, et al.
Published: (2024)

Provable Offline Reinforcement Learning for Structured Cyclic MDPs
by: Lee, Kyungbok, et al.
Published: (2026)

Sparse minimum Redundancy Maximum Relevance for feature selection
by: Naylor, Peter, et al.
Published: (2025)

Variational empirical Bayes variable selection in high-dimensional logistic regression
by: Tang, Yiqi, et al.
Published: (2025)

Fréchet random forests for metric space valued regression with non euclidean predictors
by: Capitaine, Louis, et al.
Published: (2019)

An optimal dynamic treatment regime estimator for indefinite-horizon survival outcomes
by: She, Jane, et al.
Published: (2025)

Nonparametric IPSS: Fast, flexible feature selection with false discovery control
by: Melikechi, Omar, et al.
Published: (2024)

Discriminative classification with generative features: bridging Naive Bayes and logistic regression
by: Terner, Zachary, et al.
Published: (2025)

Model-based causal feature selection for general response types
by: Kook, Lucas, et al.
Published: (2023)

A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression
by: Kim, Youngseok, et al.
Published: (2022)

Enhancing reliability in prediction intervals using point forecasters: Heteroscedastic Quantile Regression and Width-Adaptive Conformal Inference
by: Sebastián, Carlos, et al.
Published: (2024)

Boosting methods for interval-censored data with regression and classification
by: Bian, Yuan, et al.
Published: (2026)

Network-based Neighborhood regression
by: Zhen, Yaoming, et al.
Published: (2024)

Progression: an extrapolation principle for regression
by: Buriticá, Gloria, et al.
Published: (2024)

Targeted learning of heterogeneous treatment effect curves for right censored or left truncated time-to-event data
by: Pryce, Matthew, et al.
Published: (2026)

Bounds for the regression parameters in dependently censored survival models
by: Willems, Ilias, et al.
Published: (2025)

On feature selection in double-imbalanced data settings: a Random Forest approach
by: Demaria, Fabio
Published: (2025)

Estimating heterogeneous treatment effects with survival outcomes via a deep survival learner
by: Sun, Yuming, et al.
Published: (2026)

Semi-supervised learning for linear extremile regression
by: Jiang, Rong, et al.
Published: (2025)

Fast approximative estimation of conditional Shapley values when using a linear regression model or a polynomial regression model
by: Aanes, Fredrik Lohne
Published: (2025)

High-dimensional ridge regression with random features for non-identically distributed data with a variance profile
by: Dabo, Issa-Mbenard, et al.
Published: (2025)

Convex estimation of Gaussian graphical regression models with covariates
by: Liu, Ruobin, et al.
Published: (2024)