Invited Talk by Yisong Yue
Talk Title: Machine Teaching for Human Learners
Machine teaching pertains to the problem of algorithmically selecting training examples to best teach a learner. While there has been substantial progress in machine teaching recent years, many existing methods are developed based on idealized assumptions of the learner, particularly for human learners. In this talk, I will describe two recent projects in developing machine teaching techniques that are better suited for human learners: interpretable machine teaching via explanations, and adaptive machine teaching for forgetful learners.
Yisong Yue is an assistant professor in the Computing and Mathematical Sciences Department at the California Institute of Technology. He was previously a research scientist at Disney Research. Before that, he was a postdoctoral researcher in the Machine Learning Department and the iLab at Carnegie Mellon University. He received a Ph.D. from Cornell University and a B.S. from the University of Illinois at Urbana-Champaign.
Yisong's research interests lie primarily in the theory and application of statistical machine learning. He is particularly interested in developing novel methods for interactive machine learning and structured prediction. In the past, his research has been applied to information retrieval, recommender systems, text classification, learning from rich user interfaces, analyzing implicit human feedback, data-driven animation, behavior analysis, sports analytics, policy learning in robotics, and adaptive planning & allocation problems.