Lipschitz Continuity in Deep Learning: A Systematic Review of Theoretical Foundations, Estimation Methods, Regularization Approaches, and Certifiable Robustness
arXiv 2607.16329•483d33f0e6019d1bb9cca769b16bfe86b5b1612a8e2b28ee34463f0608a240b1
Lipschitz-continuitybackprop-freebayesian-experimental-designcausal-inferenceconformal-predictiondeep-learningdiffusion-modelsforward-mode-autodiffgenerative-modelingmachine-learningmeta-learningoptimizationreinforcement-learningrobustnessschrodinger-bridgesemi-supervised-learningtheoryuncertainty-quantification
Paper metadata
- arXiv ID
- 2607.16329
- Version
- Not specified by this published record
- Category
- Statistics — Machine Learning (stat.ML)
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Evidence and limitations
- Source ID
- arxiv_stat_ml
- Record identifier
- 483d33f0e6019d1bb9cca769b16bfe86b5b1612a8e2b28ee34463f0608a240b1
- Enrichment time
- 2026-07-21T07:23:54Z
- AI-assisted enrichment
- Yes
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