A unified framework for knowledge intensive gradient boosting: Leveraging human experts for noisy sparse domains

Abstract

Incorporating richer human inputs including qualitative constraints such as monotonic and synergistic influences has long been adapted inside AI. Inspired by this, we consider the problem of using such influence statements in the successful gradient-boosting framework. We develop a unified framework for both classification and regression settings that can both effectively and efficiently incorporate such constraints to accelerate learning to a better model. Our results in a large number of standard domains and two particularly novel realworld domains demonstrate the superiority of using domain knowledge rather than treating the human as a mere labeler.