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Sparse Priors for Efficient Distribution Learning
Author1, Author2, Author3, Author4, Author5
distribution learningBayesian methodssparsitygenerative AI
2609.20883
Builder Relevance
1h ago70%
Abstract
This paper introduces sparse priors to improve distribution learning efficiency in high-dimensional spaces.
Reality Card
Core Claim
Learning under a $k$-sparse prior achieves a Bayesian risk lower bound of $ ext{Ω(√(k/n))}$, effectively overcoming the curse of dimensionality.
Method / Result
Achieves a Bayesian risk lower bound of Ω(√(k/n)) under common distance metrics.
Limitations
The results depend on mild additional assumptions which may limit generalizability.
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