Bayes-THIS applies sparse Bayesian regression with automatic relevance determination to infer hypergraph structure from dynamical data and proves that Taylor expansions create indistinguishable spurious pairwise terms when higher-order interactions concentrate on nodes lacking lower-order links.
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PPAT residualizes losses via a prediction-powered control variate inside LURE, yielding lower-variance unbiased risk estimates, tailored acquisition, and asymptotic CIs that cover with fewer labels.
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Bayesian hypergraph inference from scarce and noisy dynamical observations
Bayes-THIS applies sparse Bayesian regression with automatic relevance determination to infer hypergraph structure from dynamical data and proves that Taylor expansions create indistinguishable spurious pairwise terms when higher-order interactions concentrate on nodes lacking lower-order links.
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Prediction-Powered Active Testing
PPAT residualizes losses via a prediction-powered control variate inside LURE, yielding lower-variance unbiased risk estimates, tailored acquisition, and asymptotic CIs that cover with fewer labels.