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Sieve-free ReLU Bayesian priors with near-minimax posterior contraction
We establish fractional-posterior contraction for shallow ReLU networks in Gaussian random-design regression with a $β$-Hölder target, using priors independent of the sample size and smoothness parameters. For any fixed design law $P_X$ on $[0,1]^d$, we obtain contraction in Rényi divergence and, af
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