The paper introduces Resolution-Aware Experimental Design (RAED), a method that selects experiments by minimizing the expected size of the nonempty structural candidate set while controlling false-exclusion rates. RAED is shown to preserve expected ordering under a composite Blackwell comparison and is implemented via a learned score-based approach with finite-sample nuisance-average and positive-tail calibration. Experiments on subsurface-flow, fluvial, and methane-oxidation benchmarks demonstrate RAED’s ability to resolve structural ambiguities and provide finite-sample guarantees for tail-sensitive nuisance risk.
By Sofianos Panagiotis Fotias
arXiv:2604. 11305v3 Announce Type: replace Abstract: Conformal selection (CS) uses calibration data to identify test inputs whose unobserved outcomes are likely to satisfy a pre-specified minimal quality requirement, while controlling the false discovery rate (FDR).
By Meiyi Zhu, Osvaldo Simeone
arXiv:2607. 11920v1 Announce Type: cross Abstract: Evaluating decisions made under uncertainty is hard when labeled outcomes are scarce, costly, or confounded with luck.
By Jeff Helzner
arXiv:2606. 04804v1 Announce Type: new Abstract: Generative models -- diffusion and flow matching -- are increasingly used to solve partial differential equation (PDE) inverse problems, enforcing the governing physics as a \emph{hard constraint} (via projection or guidance) and reporting the resulting samples as a Bayesian posterior with calibrated uncertainty.
By Jian Xu, Delu Zeng, John Paisley, Qibin Zhao
arXiv:2607. 16813v1 Announce Type: new Abstract: Sparse-support uncertainty is usually quantified by treating the dictionary as known, an assumption that can produce overconfident, label-dependent conclusions when the dictionary is learned from latent sparse mixtures.
By Guan-Ju Peng
arXiv:2608. 06262v1 Announce Type: new Abstract: Model evaluations may fix all tests before observing any responses or select later tests using earlier responses.
By Zonghuan Xu