SOURCE-LINKED INTELLIGENCE

Quantifying the Value of Privileged Information Using a PAC-Bayesian Approach

arXiv · AI, language, vision and robotics · article · Sep 11, 2026 · UTC

In practice, various learning scenarios provide access to auxiliary features exclusively during training. Incorporating such data to enhance model performance gave rise to a paradigm known as Learning Using Privileged Information (LUPI). While this extra information is intended to improve the resulting model, establishing a generalized, cohesive understanding of how privileged information (PI) transfers useful knowledge remains a challenge. Vapnik's original theory and subsequent works offer performance guarantees in certain cases, but these results are inherently per-algorithm and rely on set

Read original source ↗ Open in workspace

recordType

paper

region

Global