A renormalization-group inspired lattice-based framework for piecewise generalized linear models
We formally introduce a class of models inspired by renormalization group (RG) theory, built on additive hierarchical expansions analogous to those appearing in functional ANOVA and mixed-effects models. Like ReLU convolutional neural networks, they are almost everywhere locally linear; unlike ReLU networks, their partition structure is explicit, interpretable, and easy to modify or constrain. In these models, one defines a multidimensional lattice partition of the input space and uses it to scaffold variations in regression parameters. Each dimension of the lattice corresponds to an attribute by which the statistics of the problem may vary. The parameters are themselves expressed in the ...
| Authors | Joshua C. Chang |
|---|---|
| Primary category | stat.ME |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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