Interactive Learning from Sparse and Diverse Feedback
Technical Report,01 Sep 2017,30 Sep 2019
Carnegie Mellon University Pittsburgh United States
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Despite the great success of machine learning, most learning algorithms remain primarily non-interactive. Human learning, on the other hand, is highly interactive. As learners we are inquisitive - we ask questions about the data shown to us and employ feedback to decide what questions would yield answers that are most informative for the learning task at hand. In this project, we developed a suite of data-efficient interactive machine learning algorithms that employ judicious choice of what data to collect. This includes development of new algorithms as well as integration of algorithms developed in PIs prior works.