A Unified Descriptive-Complexity Framework for Model Selection under Correlated Designs

arXiv:2608.26618 · stat.ML, cs.LG · Submitted 2026-08-27 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-08-27

Updated: 2026-08-27

Terminology

• Consistency — 29× in this paper · explained in 3 episodes
• Benchmark — 21× in this paper · explained in 2 episodes
• Adaptation — 13× in this paper · explained in 1 episode
• Pruning — 13× in this paper · explained in 4 episodes
• Accuracy — 10× in this paper · explained in 1 episode
• Sparsity — 9× in this paper · explained in 4 episodes
• Admissibility — 4× in this paper · explained in 2 episodes
• Descriptive-Complexity Information Criterion — 3× in this paper
• Verification — 3× in this paper · explained in 2 episodes
• Theoretical Guarantees — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Unified Framework — 2× in this paper · explained in 5 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Copula — 1× in this paper · explained in 2 episodes
• Mixed-Integer Optimization (MIO) — 1× in this paper · explained in 2 episodes
• Phase Transitions — 1× in this paper · explained in 2 episodes
• Sample Complexity — 1× in this paper · explained in 15 episodes

Sources

• Optimality and computational barriers in variable selection under dependence
• Best subset selection is robust against design dependence

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