Computer and Systems Engineering Seminar Series presents: Machine Teaching and its Applications

I describe a branch of machine learning known as machine teaching. It is an inverse problem of learning: Given a desired target model, find a training set from which a learning algorithm learns the model. "He already has the model, what's the fuss about?" The audience murmured. You see, new applications are enabled by machine teaching: (1) We start to debug the machine learning pipeline just like we debug software, enhancing the provenance and reproducibility of data science; (2) We reveal serendipitous adversarial attacks, going beyond tweaking test items; (3) We improve human learning, optimizing the lecture based on cognitive models of the student. Beyond applications, machine teaching poses challenging questions in optimization and theory. Its computation involves combinatorial bilevel optimization, and the theory centers on teaching dimension whose relation to VC-dimension remains open. I hope you will find machine teaching interesting, and invite you to join the research.