A Unified Framework for Knowledge Intensive Gradient Boosting: Leveraging Human Experts for Noisy Sparse Domains

  • Harsha Kokel The University of Texas at Dallas
  • Phillip Odom Georgia Tech Research Institute
  • Shuo Yang LinkedIn Corporation
  • Sriraam Natarajan The University of Texas at Dallas

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 real-world domains demonstrate the superiority of using domain knowledge rather than treating the human as a mere labeler.

Published
2020-04-03
Section
AAAI Technical Track: Machine Learning