Junchi Yan, Yu Wang, et al.
IJCAI 2013
Active learning has been extensively studied and shown to be useful in solving real problems. The typical setting of traditional active learning methods is querying labels from an oracle. This is only possible if an expert exists, which may not be the case in many real world applications. In this paper, we focus on designing easier questions that can be answered by a non-expert. These questions poll relative information as opposed to absolute information and can be even generated from side-information. We propose an active learning approach that queries the ordering of the importance of an instance's neighbors rather than its label. We explore our approach on real datasets and make several interesting discoveries including that querying neighborhood information can be an effective question to ask and sometimes can even yield better performance than querying labels.
Junchi Yan, Yu Wang, et al.
IJCAI 2013
Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
Jingrui He, Wei Shen, et al.
IJCAI 2013
Robert Farrell, Rajarshi Das, et al.
AAAI-SS 2010