TRACE creates valid conformal prediction sets for complex generative models by scoring outputs via averaged denoising or velocity errors along stochastic transport paths instead of likelihoods.
Probabilis- tic conformal prediction using conditional random samples
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4verdicts
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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.
Introduces Conformal ReLiability (CReL) framework to compute a conformal-prediction-based reliability score measuring worst-case performance in prediction sets for conditional generative models.
CONTRA generates sharp multi-dimensional conformal prediction regions by defining nonconformity scores as distances from the center in the latent space of a normalizing flow.
citing papers explorer
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TRACE: Transport Alignment Conformal Prediction via Diffusion and Flow Matching Models
TRACE creates valid conformal prediction sets for complex generative models by scoring outputs via averaged denoising or velocity errors along stochastic transport paths instead of likelihoods.
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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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Conformal Reliability: A New Evaluation Metric for Conditional Generation
Introduces Conformal ReLiability (CReL) framework to compute a conformal-prediction-based reliability score measuring worst-case performance in prediction sets for conditional generative models.
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CONTRA: Conformal Prediction Region via Normalizing Flow Transformation
CONTRA generates sharp multi-dimensional conformal prediction regions by defining nonconformity scores as distances from the center in the latent space of a normalizing flow.