Machine Teaching: An Inverse Problem to Machine Learning and an Approach toward Optimal Education by Xiaojin Zhu, Machine Teaching: A New Paradigm for Building Machine Learning Systems by Patrice Y. Simard, Saleema Amershi, David M. Chickering, Alicia Edelman Pelton, Soroush Ghorashi, Christopher Meek, Gonzalo Ramos, Jina Suh, Johan Verwey, Mo Wang and John Wernsing, An Overview of Machine Teaching by Xiaojin Zhu, Adish Singla, Sandra Zilles and Anna N. Rafferty, Teacher Improves Learning by Selecting a Training Subset by Yuzhe Ma, Robert Nowak, Philippe Rigollet, Xuezhou Zhang and Xiaojin Zhu, An Optimal Control Approach to Sequential Machine Teaching by Laurent Lessard, Xuezhou Zhang and Xiaojin Zhu, Iterative Machine Teaching by Weiyang Liu, Bo Dai, Ahmad Humayun, Charlene Tay, Chen Yu, Linda B. Smith, James M. Rehg and Le Song, Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners by Yuxin Chen, Adish Singla, Oisin M. Aodha, Pietro Perona and Yisong Yue, Towards Black-box Iterative Machine Teaching by Weiyang Liu, Bo Dai, Xingguo Li, Zhen Liu, James M. Rehg and Le Song and Teaching a Black-box Learner by Sanjoy Dasgupta, Daniel Hsu, Stefanos Poulis and Xiaojin Zhu.