Learning through advice-seeking via transfer

Abstract

Experts possess vast knowledge that is typically ignored by standard machine learning methods. This rich, relational knowledge can be utilized to learn more robust models especially in the presence of noisy and incomplete training data. Such experts are often domain but not machine learning experts. Thus, deciding what knowledge to provide is a difficult problem. Our goal is to improve the human-machine interaction by providing the expert with a machine-generated bias that can be refined by the expert as necessary. To this effect, we propose using transfer learning, leveraging knowledge in alternative domains, to guide the expert to give useful advice. This knowledge is captured in the form of first-order logic horn clauses. We demonstrate empirically the value of the transferred knowledge, as well as the contribution of the expert in providing initial knowledge, plus revising and directing the use of the transferred knowledge.