[Submitted on 8 Jul 2026]
Title:LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks
View a PDF of the paper titled LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks, by Arthur Chiron (IRIT and 8 other authors
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Abstract:While accuracy, robustness, and calibration are all essential for reliable neural networks, they are often studied separately; developing models that satisfy all three simultaneously remains a central challenge. Lipschitz-constrained models guarantee robustness by design, yet the manual selection of the Lipschitz constraint L governs the resulting accuracy-robustness trade-off, and their calibration properties remain largely underexplored. In this work, we highlight a theoretical and empirical link between the enforced Lipschitz constraint and Temperature Scaling, a state-of-the-art calibration method. Specifically, we find that for a given training scheme, there exists a non-trivial value L* that yields an out-of-the-box calibrated network, and that calibration acts as a principled criterion to select a well-defined operating point on the accuracy-robustness Pareto front. Leveraging these insights, we introduce Lipschitz Scaling Training (LiST), a novel training paradigm that iteratively adjusts the global Lipschitz constant to reach this operating point. Through a margin parameter in the training loss, LiST further enables the construction of a fully calibrated Pareto front, allowing users to navigate the accuracy-robustness trade-off while remaining calibrated throughout. At convergence, LiST also enables the reintegration of calibration data into training, improving sample efficiency without sacrificing calibration. We validate LiST on CIFAR-10/100 and Tiny-ImageNet, demonstrating competitive accuracy and robustness against constrained and unconstrained baselines, while remaining calibrated out of the box. Code is available at GitHub.
Subjects:
Machine Learning (cs.LG); Machine Learning (stat.ML)
Cite as: arXiv:2607.07745 [cs.LG]
(or arXiv:2607.07745v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.07745
arXiv-issued DOI via DataCite
Submission history
From: Arthur Chiron [view email] [via CCSD proxy] [v1] Wed, 8 Jul 2026 08:34:01 UTC (223 KB)
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