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As a result, as long as n ≥ cN ′ 3, we have ˆL(β) ≥ ˆL(β⋆)

Combining (33), (34), we have ˆL(β) > ˆL(β⋆) + λ 2 n−ϵ − c r Tr(IS) logn n c1n−δ(log n) 1 4 + n−ϵτ − B2 r log n n c1n−δ(log n) 1 4 + n−ϵτ 2 − B3 c1n−δ(log n) 1 4 + n−ϵτ 3 = ˆL(β

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Principled Out-of-Distribution Generalization via Simplicity

stat.ML · 2025-05-28 · conditional · novelty 5.0

Under the assumption that the out-of-distribution-generalizing model is the simplest source-consistent parameter, regularized maximum likelihood recovers it with near-1/n excess risk (constant gap) or n^{-1+2/(3τ)} (vanishing gap).

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  • Principled Out-of-Distribution Generalization via Simplicity stat.ML · 2025-05-28 · conditional · none · ref 5

    Under the assumption that the out-of-distribution-generalizing model is the simplest source-consistent parameter, regularized maximum likelihood recovers it with near-1/n excess risk (constant gap) or n^{-1+2/(3τ)} (vanishing gap).