First non-asymptotic sample complexity bounds for structure learning of polynomial exponential families via score matching, with polynomial dependence on model dimension.
Computational Statistics & Data Analysis , volume =
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CTEM unifies density estimation via a bounded energy-difference transform that yields a sample-only objective with constant target 1, recovering log p without partition functions or unbounded ratio regression.
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Finite Sample Bounds for Learning with Score Matching
First non-asymptotic sample complexity bounds for structure learning of polynomial exponential families via score matching, with polynomial dependence on model dimension.
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Constant-Target Energy Matching: A Unified Framework for Continuous and Discrete Density Estimation
CTEM unifies density estimation via a bounded energy-difference transform that yields a sample-only objective with constant target 1, recovering log p without partition functions or unbounded ratio regression.