JECS aggregates per-model conformal p-values via their maximum and reconstructs a conservative envelope of the max-p null distribution to select benchmarks with global contamination rate control.
Proceedings of the Sixteenth International Conference on Machine Learning , pages =
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A quantum ensemble method reduces operator inference to linear complexity and supplies distribution-free uncertainty bounds for high-dimensional dynamical systems.
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Provable Joint Decontamination for Benchmarking Multiple Large Language Models
JECS aggregates per-model conformal p-values via their maximum and reconstructs a conservative envelope of the max-p null distribution to select benchmarks with global contamination rate control.
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Conformalized Quantum DeepONet Ensembles for Scalable Operator Learning with Distribution-Free Uncertainty
A quantum ensemble method reduces operator inference to linear complexity and supplies distribution-free uncertainty bounds for high-dimensional dynamical systems.