Copula-based conformal prediction for Multi-Target Regression

TL;DR

This paper introduces a copula-based conformal prediction method using deep neural networks to produce valid and efficient multi-target regression predictions, addressing a gap in multi-task learning.

Contribution

It proposes a novel copula-based approach integrated with deep neural networks for valid multi-target conformal prediction, enhancing multi-task learning capabilities.

Findings

Ensures validity and efficiency in multi-target regression.

Performs well across various datasets.

Addresses a gap in conformal prediction for multi-task learning.

Abstract

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.