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LiD-GLM: Lipschitz-constrained Deep Generalized Linear Models — arXiv:2608.16340

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.16340
License (as archived): CC0-1.0
Archived: 2026-10-18T19:44:23.776Z
SHA-256 of the archived text: b334d672447052d144ac3664e921c6a5f6873bfa3dd4adf968ff5d25156baa2f
Stable URL of this page: https://metraipolis.com/documento/dc_fb121bc74ca8

How to cite

LiD-GLM: Lipschitz-constrained Deep Generalized Linear Models — arXiv:2608.16340 — arXiv (en), https://arxiv.org/abs/2608.16340 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fb121bc74ca8 (Metraipolis archive dc_fb121bc74ca8, sha256 b334d672447052d144ac3664e921c6a5f6873bfa3dd4adf968ff5d25156baa2f)

BibTeX

@misc{dcfb121bc74ca8,
  title = {LiD-GLM: Lipschitz-constrained Deep Generalized Linear Models — arXiv:2608.16340},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.16340},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fb121bc74ca8; sha256 b334d672447052d144ac3664e921c6a5f6873bfa3dd4adf968ff5d25156baa2f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.16340. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 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) predictor. The i-ResNets correspond to a controlled deviation from identity and by constraining their Lipschitz constant one can rigorously limit and quantify how far the hybrid model deviates from its traditional counterpart. This enables a user-specifiable compromise between flexibility and interpretability without limiting the structure of nonlinear and interaction effects that can be learned. Furthermore, we develop specific inherent interpretation techniques for our model and enforce model identifiability through an adapted post-hoc orthogonalization.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-18 in cs.LG).
Categorie arXiv: Machine Learning (stat.ML) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2608.16340 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T19:02:44.307Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-08-18?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).