Applied/ACMS/absS19

ACMS Abstracts: Spring 2019

Jerry Zhu (University of Wisconsin-Madison, CS)

Machine Teaching: Optimal Control of Machine Learning

As machine learning is increasingly adopted in science and engineering, it becomes important to take a higher level view where the machine learner is only one of the agents in a multi-agent system. Other agents may have an incentive to control the learner. As examples, in adversarial machine learning an attacker can poison the training data to manipulate the model the learner learns; in education a teacher can optimize the curriculum to enhance student (modeled as a computational learning algorithm) performance. Machine teaching is optimal control theory applied to machine learning: the plant is the learner, the state is the learned model, and the control is the training data. In this talk I survey the mathematical foundation of machine teaching and the new research frontiers opened up by this confluence of machine learning and control theory.