Probabilistic Conformal Prediction Using Conditional Random Samples
This addresses the need for more efficient and accurate uncertainty quantification in machine learning, though it appears incremental as it builds on existing conformal prediction frameworks.
The paper tackles the problem of predictive inference by proposing probabilistic conformal prediction (PCP), which constructs predictive sets using random samples from generative models, and shows that it guarantees correct marginal coverage and provides sharper predictive sets compared to existing methods.
This paper proposes probabilistic conformal prediction (PCP), a predictive inference algorithm that estimates a target variable by a discontinuous predictive set. Given inputs, PCP construct the predictive set based on random samples from an estimated generative model. It is efficient and compatible with either explicit or implicit conditional generative models. Theoretically, we show that PCP guarantees correct marginal coverage with finite samples. Empirically, we study PCP on a variety of simulated and real datasets. Compared to existing methods for conformal inference, PCP provides sharper predictive sets.