A Deep Generative Approach to Stratified Learning
While the manifold hypothesis is widely adopted in modern machine learning, complex data is often better modeled as stratified spaces -- unions of manifolds (strata) of varying dimensions. Stratified learning is challenging due to varying dimensionality, intersection singularities, and lack of efficient models in learning the underlying distributions. We provide a deep generative approach to stratified learning by developing two generative frameworks for learning distributions on stratified spaces. The first is a sieve maximum likelihood approach realized via a dimension-aware mixture of variational autoencoders. The second is a diffusion-based framework that explores the score field stru...
| Authors | Randy Martinez, Rong Tang, Lizhen Lin |
|---|---|
| Primary category | stat.ML |
| Published | 12 April 2026 |
| Last updated at source | 12 April 2026 |
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