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= X k∈K2 ωk P eJk j=1 Ey∼Qk h exp − ∥x−mty∥2 2eσ2 t · − x−mty eσ2 t i ·ρ |x−ex∗ kj | c4n−1/(2αk +dk ) max 1,P eJk j=1 ρ |x−ex∗ kj | c4n−1/(2αk +dk ), (B ′

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stat.ML 1

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2026 1

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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 10

    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.