Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Active Learning with Label Proportions. / Poyiadzi, Rafael; Santos-Rodriguez, Raul; Twomey, Niall.
2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 3097-3101 8682748 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Active Learning with Label Proportions
AU - Poyiadzi, Rafael
AU - Santos-Rodriguez, Raul
AU - Twomey, Niall
PY - 2019/5/1
Y1 - 2019/5/1
N2 - Active Learning (AL) refers to the setting where the learner has the ability to perform queries to an oracle to acquire the true label of an instance or, sometimes, a set of instances. Even though Active Learning has been studied extensively, the setting is usually restricted to assume that the oracle is trustworthy and will provide the actual label. We argue that, while common, this approach can be made more flexible to account for different forms of supervision. In this paper, we propose a new framework that allows the algorithm to request the label for a bag of samples at a time. Although this label will come in the form of proportions of class labels in the bags and therefore encode less information, we demonstrate that we can still learn effectively.
AB - Active Learning (AL) refers to the setting where the learner has the ability to perform queries to an oracle to acquire the true label of an instance or, sometimes, a set of instances. Even though Active Learning has been studied extensively, the setting is usually restricted to assume that the oracle is trustworthy and will provide the actual label. We argue that, while common, this approach can be made more flexible to account for different forms of supervision. In this paper, we propose a new framework that allows the algorithm to request the label for a bag of samples at a time. Although this label will come in the form of proportions of class labels in the bags and therefore encode less information, we demonstrate that we can still learn effectively.
KW - Active learning
KW - label proportions
KW - label propagation
UR - http://www.scopus.com/inward/record.url?scp=85068983825&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2019.8682748
DO - 10.1109/ICASSP.2019.8682748
M3 - Conference contribution
SN - 9781479981328
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 3097
EP - 3101
BT - 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -
