Document Type
Conference Proceeding
Publication Date
9-2025
Abstract
It is common in machine learning to estimate a response y given covariate information x . However, these predictions alone do not quantify any uncertainty associated with said predictions. One way to overcome this deficiency is with conformal inference methods, which construct a set containing the unobserved response with a prescribed probability. Unfortunately, even with a one-dimensional response, conformal inference is computationally expensive despite recent encouraging advances. In this paper, we explore multi-output regression, delivering exact derivations of conformal inference p-values when the predictive model can be described as a linear function of y . Additionally, we introduce a multivariate extension of rootCP as well unionCP as efficient ways of approximating the conformal prediction region for a wide array of multi-output predictors, both linear and nonlinear, while preserving computational advantages. We also provide both theoretical and empirical evidence of the effectiveness of our methods using both real-world and simulated data.
Source Publication
Proceedings of Machine Learning Research, volume 266
Recommended Citation
Johnstone, C. & Ndiaye, E.. (2025). Exact and Approximate Conformal Inference for Multi-Output Regression. Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications, in Proceedings of Machine Learning Research 266:153-172 Available from https://proceedings.mlr.press/v266/johnstone25a.html.
Comments
© 2025 C. Johnstone & E. Ndiaye.