LiD-GLM: Lipschitz-constrained Deep Generalized Linear Models
The method proposed leverages invertible residual neural networks (i-ResNets) to equip generalized linear models with both nonlinear parameter estimation and a flexible correction of their distributional assumptions while always retaining stochastic monotonicity of the modeled distribution in the (formerly linear) pred...
Tom A. Splittgerber, Niklas Koenen, Marvin N. Wright et al.
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