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This paper proposes probabilistic conformal prediction (PCP), a predictiveinference algorithm that estimates a target variable by a discontinuouspredictive set. Given inputs, PCP construct the predictive set based on randomsamples from an estimated generative model. It is efficient and compatible witheither explicit or implicit conditional generative models. Theoretically, weshow that PCP guarantees correct marginal coverage with finite samples.Empirically, we study PCP on a variety of simulated and real datasets. Comparedto existing methods for conformal inference, PCP provides sharper predictivesets.

Probabilistic Conformal Prediction Using Conditional Random Samples