A Unified Descriptive-Complexity Framework for Model Selection under Correlated Designs
Model selection becomes particularly challenging under strong predictor dependence and model-class uncertainty, especially when there are exponentially many models. We propose a Descriptive-Complexity Information Criterion (DCIC) that regularizes large candidate model collections through Kraft-admissible code lengths. Under sub-Weibull noise, we establish selection consistency through approximation-error separation without relying on RIP-type conditions, together with nonasymptotic oracle risk bounds that remain valid under model misspecification. The same coding principle places heterogeneous classes on a common complexity scale at a small additional class-identification cost. This exten...
| Authors | Yanhang Zhang, Wei Liu, Yuhong Yang |
|---|---|
| Primary category | stat.ML |
| Published | 27 August 2026 |
| Last updated at source | 27 August 2026 |
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