The choice of closeness measure in diffusion reward alignment determines the computational primitives and tractable reward classes, with linear exponential tilts sufficing for KL with convex rewards and proximal oracles for Wasserstein with concave or low-dimensional Lipschitz rewards.
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A generalized Tweedie identity and moment-generating-function representation enable nonparametric recovery of full posteriors for heteroscedastic normal means with unknown variances without specifying a prior.
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The tractability landscape of diffusion alignment: regularization, rewards, and computational primitives
The choice of closeness measure in diffusion reward alignment determines the computational primitives and tractable reward classes, with linear exponential tilts sufficing for KL with convex rewards and proximal oracles for Wasserstein with concave or low-dimensional Lipschitz rewards.
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Nonparametric f-Modeling for Empirical Bayes Inference with Unequal and Unknown Variances
A generalized Tweedie identity and moment-generating-function representation enable nonparametric recovery of full posteriors for heteroscedastic normal means with unknown variances without specifying a prior.