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.
SurvSet: An Open-Source Time-to-Event Dataset Repository
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UNVERDICTED 3representative citing papers
DeepFHT parameterizes first-hitting-time diffusion processes with neural networks to produce closed-form, interpretable survival and hazard functions that match Cox-level accuracy on tested data.
Large-scale neutral benchmark of survival models on low-dimensional right-censored data finds Cox PH performs comparably to more complex methods across discrimination, calibration, and predictive 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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Neural Diffusion Processes for Physically Interpretable Survival Prediction
DeepFHT parameterizes first-hitting-time diffusion processes with neural networks to produce closed-form, interpretable survival and hazard functions that match Cox-level accuracy on tested data.
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A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data
Large-scale neutral benchmark of survival models on low-dimensional right-censored data finds Cox PH performs comparably to more complex methods across discrimination, calibration, and predictive metrics.