Copula-based conformal prediction for Multi-Target Regression
This work addresses the need for reliable uncertainty quantification in multi-target regression, which is an incremental improvement in a niche area of machine learning.
The paper tackles the problem of providing valid multivariate predictions in multi-target regression by proposing a copula-based conformal prediction method using deep neural networks, and it demonstrates that this approach ensures efficiency and validity across various datasets.
There are relatively few works dealing with conformal prediction for multi-task learning issues, and this is particularly true for multi-target regression. This paper focuses on the problem of providing valid (i.e., frequency calibrated) multi-variate predictions. To do so, we propose to use copula functions applied to deep neural networks for inductive conformal prediction. We show that the proposed method ensures efficiency and validity for multi-target regression problems on various data sets.