SOURCE-LINKED INTELLIGENCE
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
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T05:02:14.000Z
First collected: 2026-09-21T08:51:59.673Z. This is not the publication date.