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We present neural frailty machine (NFM), a powerful and flexible neuralmodeling framework for survival regressions. The NFM framework utilizes theclassical idea of multiplicative frailty in survival analysis to captureunobserved heterogeneity among individuals, at the same time being able toleverage the strong approximation power of neural architectures for handlingnonlinear covariate dependence. Two concrete models are derived under theframework that extends neural proportional hazard models and nonparametrichazard regression models. Both models allow efficient training under thelikelihood objective. Theoretically, for both proposed models, we establishstatistical guarantees of neural function approximation with respect tononparametric components via characterizing their rate of convergence.Empirically, we provide synthetic experiments that verify our theoreticalstatements. We also conduct experimental evaluations over $6$ benchmarkdatasets of different scales, showing that the proposed NFM models outperformstate-of-the-art survival models in terms of predictive performance. Our codeis publicly availabel at https://github.com/Rorschach1989/nfm

Neural Frailty Machine: Beyond proportional hazard assumption in neural survival regressions