About the Workshop

In many real-world problems, unlabeled data are often abundant whereas labeled data are scarce. Label acquisition is usually expensive due to the involvement of human experts, and thus, it is important to train an accurate prediction model by a small number of labeled instances. Active learning aims at reducing human efforts on annotating examples in a machine learning system, and has been successfully applied into various real tasks. The aim of this workshop is to provide a forum for researchers and practitioners to discuss on active learning and other related topics.


Sheng-Jun Huang, Nanjing University of Aeronautics and Astronautics
Hsuan-Tien Lin, National Taiwan University


August 28, 2018

14:00-14:10 Opening
14:10-14:55 Prototype Selection in Machine Learning: Modeling, Algorithm and Applications
Zhenfeng Zhu, Beijing Jiaotong University
14:55-15:40 Active Learning for Discriminative Network Representations
Chuan Zhou, Institute of Information Engineering, Chinese Academy of Sciences
15:40-16:00 Break
16:00-16:45 Active feature acquisition with supervised matrix completion
Miao Xu, RIKEN Center for Advanced Intelligence Project, Japan
16:45-17:30 The Bernoulli Trick for Uncertainty Sampling
Hanmo Wang, Institute of Software, Chinese Academy of Sciences