pith. sign in

Then we will use Lemmas 26, 27 and 28 for the approximationm t,σ t, monomial and reciprocal function

= X k∈K2 eJkX j=1 ωkρ |x−ex∗ kj | c4n−1/(2αk +dk ) ρ |x−Gkj(ϕp kj(x))| c6eσti √logn max 1,P eJk j=1 ρ |x−x∗ kj | c4n−1/(2αk +dk ) ·exp − ∥x−G kj(ϕp kj(x))∥2 2eσ2 t · L1X l1=0 L2X l2=0 1 eσt 2l1+2l2 X 0≤s1≤2l1+l2 ms1 t X s2∈N dk 0 ,|s2|≤(2l1 · 2023

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A Deep Generative Approach to Stratified Learning

stat.ML · 2026-04-12 · unverdicted · novelty 6.0

Two generative frameworks (sieve MLE via dimension-aware VAE mixtures and diffusion on score fields) are developed for distributions on stratified spaces, with convergence rates depending on intrinsic dimensions and smoothness, plus consistent estimators for the number and dimensions of strata.

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  • A Deep Generative Approach to Stratified Learning stat.ML · 2026-04-12 · unverdicted · none · ref 19

    Two generative frameworks (sieve MLE via dimension-aware VAE mixtures and diffusion on score fields) are developed for distributions on stratified spaces, with convergence rates depending on intrinsic dimensions and smoothness, plus consistent estimators for the number and dimensions of strata.