IN COUNJUCTION WITH
The 15th Pacific Rim International Conference on Artificial Intelligence (PRICAI) August 28-31, 2018, Nanjing, China
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.
Co-Chairs
Sheng-Jun Huang, Nanjing University of Aeronautics and Astronautics
Hsuan-Tien Lin, National Taiwan University
Schedule
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 |