Differentiable nonconformity scores induce flows that sample conformal prediction set boundaries, and mixing flows across levels produces conformal predictive distributions whose quantiles match the sets.
Strictly proper scoring rules, prediction, and estimation
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Self-consistency training on real data improves amortized Bayesian model comparison accuracy under distribution shifts, especially in open-world misspecification when analytic or locally accurate surrogate likelihoods are available.
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Flow-Based Conformal Predictive Distributions
Differentiable nonconformity scores induce flows that sample conformal prediction set boundaries, and mixing flows across levels produces conformal predictive distributions whose quantiles match the sets.
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Improving the Accuracy of Amortized Model Comparison with Self-Consistency
Self-consistency training on real data improves amortized Bayesian model comparison accuracy under distribution shifts, especially in open-world misspecification when analytic or locally accurate surrogate likelihoods are available.