TY - GEN
T1 - Learning from data heterogeneity
T2 - 26th International Joint Conference on Artificial Intelligence, IJCAI 2017
AU - He, Jingrui
N1 - Funding Information:
This work is supported by National Science Foundation under Grant No. IIS-1552654, ONR under Grant No. N00014-15-1-2821, and an IBM Faculty Award. The views and conclusions are those of the authors and should not be interpreted as representing the official policies of the funding agencies or the government.
PY - 2017
Y1 - 2017
N2 - Nowadays, as an intrinsic property of big data, data heterogeneity can be seen in a variety of realworld applications, ranging from security to manufacturing, from healthcare to crowdsourcing. It refers to any inhomogeneity in the data, and can be present in a variety of forms, corresponding to different types of data heterogeneity, such as task/view/instance/oracle heterogeneity. As shown in previous work as well as our own work, learning from data heterogeneity not only helps people gain a better understanding of the large volume of data, but also provides a means to leverage such data for effective predictive modeling. In this paper, along with multiple real applications, we will briefly review state-of-the-art techniques for learning from data heterogeneity, and demonstrate their performance at addressing these real world problems.
AB - Nowadays, as an intrinsic property of big data, data heterogeneity can be seen in a variety of realworld applications, ranging from security to manufacturing, from healthcare to crowdsourcing. It refers to any inhomogeneity in the data, and can be present in a variety of forms, corresponding to different types of data heterogeneity, such as task/view/instance/oracle heterogeneity. As shown in previous work as well as our own work, learning from data heterogeneity not only helps people gain a better understanding of the large volume of data, but also provides a means to leverage such data for effective predictive modeling. In this paper, along with multiple real applications, we will briefly review state-of-the-art techniques for learning from data heterogeneity, and demonstrate their performance at addressing these real world problems.
UR - http://www.scopus.com/inward/record.url?scp=85031929973&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85031929973&partnerID=8YFLogxK
U2 - 10.24963/ijcai.2017/735
DO - 10.24963/ijcai.2017/735
M3 - Conference contribution
AN - SCOPUS:85031929973
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 5126
EP - 5130
BT - 26th International Joint Conference on Artificial Intelligence, IJCAI 2017
A2 - Sierra, Carles
PB - International Joint Conferences on Artificial Intelligence
Y2 - 19 August 2017 through 25 August 2017
ER -