SurvivalPFN amortizes Bayesian survival analysis for right-censored data by pretraining a prior-data fitted network on synthetic identifiable DGPs and then performing in-context inference, achieving competitive results on 61 real datasets.
Deep Neural Networks for Survival Analysis Based on a Multi-Task Framework
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abstract
Survival analysis/time-to-event models are extremely useful as they can help companies predict when a customer will buy a product, churn or default on a loan, and therefore help them improve their ROI. In this paper, we introduce a new method to calculate survival functions using the Multi-Task Logistic Regression (MTLR) model as its base and a deep learning architecture as its core. Based on the Concordance index (C-index) and Brier score, this method outperforms the MTLR in all the experiments disclosed in this paper as well as the Cox Proportional Hazard (CoxPH) model when nonlinear dependencies are found.
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Survival models show distinct interpolation behaviors driven by loss functions, so overparametrization does not reliably improve generalization as in regression or classification.
QSurv uses Gauss-Legendre numerical quadrature and time-conditioned low-rank adaptation to enable scalable nonparametric continuous-time survival modeling with theoretical error bounds.
Deep survival models for Alzheimer's progression are robust but exhibit considerable bias across sensitive attributes, which the authors quantify using two newly proposed fairness metrics.
citing papers explorer
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SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference
SurvivalPFN amortizes Bayesian survival analysis for right-censored data by pretraining a prior-data fitted network on synthetic identifiable DGPs and then performing in-context inference, achieving competitive results on 61 real datasets.
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Understanding Overparametrization in Survival Models through Interpolation
Survival models show distinct interpolation behaviors driven by loss functions, so overparametrization does not reliably improve generalization as in regression or classification.
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A Scalable Nonparametric Continuous-Time Survival Model through Numerical Quadrature
QSurv uses Gauss-Legendre numerical quadrature and time-conditioned low-rank adaptation to enable scalable nonparametric continuous-time survival modeling with theoretical error bounds.
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Investigating Trustworthiness of Nonparametric Deep Survival Models for Alzheimer's Disease Progression Analysis
Deep survival models for Alzheimer's progression are robust but exhibit considerable bias across sensitive attributes, which the authors quantify using two newly proposed fairness metrics.