{"type":"ScholarlyArticle","title":"Learning from Data Heterogeneity: Algorithms and Applications","authors":[{"name":"Jingrui He","slug":"jingrui-he","given":"Jingrui","family":"He","affiliation":"School of Computing, Informatics, and Decision Systems Engineering , Arizona State University"}],"abstract":"Nowadays, as an intrinsic property of big data, data heterogeneity can be seen in a variety of real-world 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.","doi":"10.24963/ijcai.2017/735","url":"https://ijcai.org/proceedings/2017/735","pdf":"https://ijcai.org/proceedings/2017/0735.pdf","markdown":"https://ijcai.org/proceedings/2017/735.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/735","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra, IIIA-CSIC","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Early Career","track":"Early Career","number":735,"pages":{"first":5126,"last":5130},"keywords":["Machine Learning: Transfer, Adaptation, Multi-task Learning","Machine Learning: Time-series/Data Streams","Machine Learning: Multi-instance/Multi-label/Multi-view learning"],"videos":[],"references":[{"position":1,"raw":"[Albert, 1988] James H. 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TKDD, 10(4):43:1-43:18, 2016.","title":"Co-clustering structural temporal data with applications to semiconductor manufacturing","authors":"He Zhu, Yada Zhu, Jingrui He","year":"2016","venue":"TKDD","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Zhu et al., 2015] Yada Zhu, Hongxia Yang, and Jingrui He. Co-clustering based dual prediction for cargo pricing op- timization. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, August 10-13, 2015, pages 1583-1592, 2015.","title":"Co-clustering based dual prediction for cargo pricing optimization","authors":"Yada Zhu, Hongxia Yang, Jingrui He","year":"2015","venue":"Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}