报告摘要：Interactive Machine Learning is an iterative learning process that tightly couples a human with a machine learner, which is widely used by researchers and practitioners to effectively solve a wide variety of real-world application problems. This talk presents the motivation, major challenges of interactive machine learning. Several typical techniques and examples, such as interactive data quality improvement, explainable machine learning, and visual text analytics, are also introduced to deomonstrate how interactive machine learning enables people to effectively interact with machine learning models for better decisions.
讲者简介：Shixia Liu is a tenured associate professor at Tsinghua University. Her research interests include visual text analytics, visual social analytics, visual behavior analytics, graph visualization, and tree visualization. Before joining Tsinghua University, she worked as a lead researcher at Microsoft Research Asia and a research staff member at IBM China Research Lab. Shixia is one of the Papers Co-Chairs of IEEE VAST 2016 and 2017. She is an associate editor of IEEE Transactions on Visualization and Computer Graphics, IEEE Transactions on Big Data, and is on the editorial board of Information Visualization. She was the guest editor of ACM Transactions on Intelligent Systems and Technology and Tsinghua Science and Technology. She was the program co-chair of PacifcVis 2014 and VINCI 2012. Shixia was in the Steering Committee of VINCI 2013. She is on the organizing committee of IEEE VIS 2015 and 2014. She is/was in the Program Committee for CHI 2019, 2018, InfoVis 2015, 2014, VAST 2018, 2015, 2014, KDD 2015, 2014, 2013, ACM Multimedia 2009, SDM 2008, ACM IUI 2011, 2009, PacificVis 2008, 2009, 2010, 2011, PAKDD 2013, VISAPP 2012, 2011, VINCI 2011.