LiD-GLM: Lipschitz-constrained Deep Generalized Linear Models
The combination of traditional statistical models and neural network (NN) components into semi-structured hybrid models is an intriguing approach to construct models that, ideally, combine traditional interpretability with the unprecedented flexibility of NNs. In order to preserve interpretability, it is usually necessary to restrict the NN components to prevent them from dominating the model. However, existing methods that enforce structural constraints on their NN components severely limit their models' flexibility; in contrast, methods that only enforce weak, indirect constraints lose meaningful interpretability. The method we propose therefore leverages invertible residual neural netw...
| Authors | Tom Splittgerber, Niklas Koenen, Marvin N. Wright et al. |
|---|---|
| Primary category | stat.ML |
| Published | 17 August 2026 |
| Last updated at source | 25 September 2026 |
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