arXiv Machine Learning August 18, 2026 By Tom Splittgerber, Niklas Koenen, Marvin N. Wright, Werner Brannath

LiD-GLM: Lipschitz-constrained Deep Generalized Linear Models

Read the original on arXiv Machine Learning →

arXiv:2608. 16340v1 Announce Type: cross Abstract: 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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arXiv AI

Jul 13

Principles of Lipschitz continuity in neural networks

arXiv:2602. 04078v2 Announce Type: replace-cross Abstract: Deep learning has achieved remarkable success across a wide range of domains, significantly expanding the frontiers of what is achievable in artificial intelligence.

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arXiv Machine Learning

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Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization

arXiv:2606. 29951v1 Announce Type: new Abstract: Interpretable Mesomorphic Neural Networks (IMNs) offer a promising framework that combines the predictive power of deep neural networks with the interpretability of linear models.

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arXiv Machine Learning

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Generalized Convexity and Smoothness via Conjugate Duality: Optimization Theory for Deep Neural Networks

arXiv:2608. 09523v1 Announce Type: new Abstract: Deep neural network (DNN) training with stochastic gradient descent (SGD) and its variants achieves strong empirical performance, yet classical optimization theory does not fully explain this success.

By Binchuan Qi

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arXiv AI

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Directional Influence Function: Estimating Training Data Influence in Constrained Learning

arXiv:2607. 23388v1 Announce Type: cross Abstract: As constrained learning becomes increasingly common, models are trained under explicit feasibility requirements to enforce fairness, safety, robustness, regulariza- tion, and physics or logic constraints.

By Xin Wang (Jeff), R. Tyrrell Rockafellar (Jeff), Xuegang (Jeff), Ban

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arXiv Machine Learning

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Mitigating the Curse of Dimensionality in Uniform Convergence of Deep Neural Networks via Smooth Activations

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By Yizhe Ding, Runze Li, Jia Liu, Lingzhou Xue

arXiv Machine Learning

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Lipschitz Continuity in Deep Learning: A Systematic Review of Theoretical Foundations, Estimation Methods, Regularization Approaches, and Certifiable Robustness

arXiv:2607. 16329v1 Announce Type: cross Abstract: Lipschitz continuity is a fundamental property of neural networks that characterizes their sensitivity to input perturbations.

By R\'ois\'in Luo, James McDermott, Colm O'Riordan

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Principles of Lipschitz continuity in neural networks

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