Conformal prediction provides a principled framework for constructing predictive sets with finite-
sample validity. While much of the focus has been on univariate response variables, existing mul-
tivariate methods either impose rigid geometric assumptions or rely on flexible but computation-
ally expensive approaches that do not explicitly optimize prediction set volume. We propose an
optimization-driven framework based on a novel loss function that directly learns minimum-volume
covering sets while ensuring valid coverage. This formulation naturally induces a new nonconfor-
mity score for conformal prediction, which adapts to the residual distribution and covariates. Our
approach optimizes over prediction sets defined by arbitrary norm balls—including single and multi-
norm formulations. Additionally, by jointly optimizing both the predictive model and predictive
uncertainty, we obtain prediction sets that are tight, informative, and computationally efficient, as
demonstrated in our experiments on real-world datasets.
