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Learning concepts from natural high-dimensional data (e.g., images) holds potential

Modern neural networks often have great expressive power and can be trained to overfit the training data, while still achieving a good test performance.

Hierarchical Bayesian methods enable information sharing across regression problems on multiple groups of data.

The NFM framework utilizes the classical idea of multiplicative frailty in survival analysis as a principled way of extending the proportional hazard assumption, at the same time being able to leverage the strong approximation power of neural architectures for handling nonlinear covariate dependence.

We individually consider the gap-independent vs. gap-dependent

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