Dimension-free uniform concentration bound for logistic regression
Abstract
We provide a novel dimension-free uniform concentration bound for the empirical risk function of constrained logistic regression. Our bound yields a milder sufficient condition for a uniform law of large numbers than conditions derived by the Rademacher complexity argument and McDiarmid's inequality. The derivation is based on the PAC-Bayes approach with second-order expansion and Rademacher-complexity-based bounds for the residual term of the expansion.
Fields of Study & Subjects
Statistical Methods and Inference
Advanced Statistical Process Monitoring
Publication details
- Journal / source
- Bernoulli
- Volume
- 32
- Issue
- 3
- Publication date
- 29 April 2026
- Language
- EN