WHL 2014 : SDM 2014 Workshop on Heterogeneous Learning
Call For Papers
The main objective of this workshop is to bring the attention of researchers to real problems with multiple types of heterogeneities, ranging from online social media analysis, traffic prediction, to the manufacturing process, brain image analysis, etc. Some commonly found heterogeneities include task heterogeneity (as in multi-task learning), view heterogeneity (as in multi-view learning), instance heterogeneity (as in multi-instance learning), label heterogeneity (as in multi-label learning), oracle heterogeneity (as in crowdsourcing), etc. In the past years, researchers have proposed various techniques for modeling a single type of heterogeneity as well as multiple types of heterogeneities.
This workshop focuses on novel methodologies, applications and theories for effectively leveraging these heterogeneities. Here we are facing multiple challenges. To name a few: (1) how can we effectively exploit the label/example structure to improve the classification performance; (2) how can we handle the class imbalance problem when facing one or more types of heterogeneities; (3) how can we improve the effectiveness and efficiency of existing learning techniques for large-scale problems, especially when both the data dimensionality and the number of labels/examples are large; (4) how can we jointly model multiple types of heterogeneities to maximally improve the classification performance; (5) how do the underlying assumptions associated with multiple types of heterogeneities affect the learning methods.
We encourage submissions on a variety of topics, including but not limited to:
(1) Novel approaches for modeling a single type of heterogeneity, e.g., task/view/instance/label/oracle heterogeneities.
(2) Novel approaches for simultaneously modeling multiple types of heterogeneities, e.g., multi-task multi-view learning to leverage both the task and view heterogeneities.
(3) Novel applications with a single or multiple types of heterogeneities.
(4) Systematic analysis regarding the relationship between the assumptions underlying each type of heterogeneity and the performance of the predictor;
For this workshop, the potential participants and target audience would be faculty, students and researchers in related areas, e.g., multi-task learning, multi-view learning, multi-instance learning, multi-label learning, etc. We also encourage people with application background to actively participate in this workshop.
We believe that advancements on these topics will benefit a variety of application domains.
Paper Submission Return to Top
01/10/2014: Paper Submission
01/31/2014: Author Notification
02/10/2014: Camera Ready Paper Due
Paper Submission Instructions
Papers submitted to this workshop should be limited to 6 pages formatted using the SIAM SODA macro (http://www.siam.org/proceedings/macros.php). Authors are required to submit their papers electronically in PDF format to email@example.com by 11:59pm EST, January 10, 2014.
Jieping Ye (Arizona State University): firstname.lastname@example.org
Jieping Ye is an Associate Professor of Computer Science and Engineering at the Arizona State University. He is a core faculty member of the Bio-design Institute at ASU. He received his Ph.D. degree in Computer Science from University of Minnesota, Twin Cities in 2005. His research interests include machine learning, data mining, and biomedical informatics. He has served as Senior Program Committee/Area Chair/Program Committee Vice Chair of many conferences including NIPS,
KDD, IJCAI, ICDM, SDM, ACML, and PAKDD. He serves as an Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence. He won the SCI Young Investigator of the Year Award at ASU in 2007, the SCI Researcher of the Year Award
at ASU in 2009, and the NSF CAREER Award in 2010. His papers have been selected for the outstanding student paper at the International Conference on Machine Learning in 2004, the KDD best research paper honorable mention in 2010, the KDD best research paper nomination in 2011 and 2012, the SDM best research paper runner up in 2013, and the KDD best research paper runner up in 2013.
Yuhong Guo (Temple University): email@example.com
Yuhong Guo is an Assistant Professor in the Department of Computer and Information Sciences at Temple University. She has previously been a Research Fellow at the Australian National University and a Postdoctoral Fellow at the University of Alberta. Her research interests include machine learning, natural language processing, computer vision, bioinformatics and data mining. She has received the Distinguished Paper Award from the International Joint Conference on Artificial Intelligence in 2005 and the Outstanding Paper Award from the AAAI Conference on Artificial Intelligence in 2012. She has served in program committees of many conferences, including NIPS, ICML, UAI, AAAI, IJCAI, ACML and SDM.
Jingrui He (Stevens Institute of Technology): firstname.lastname@example.org
Jingrui He is an Assistant Professor in the Computer Science Department at Stevens Institute of Technology. Before joining Stevens, she was a Research Staff Member at IBM T.J. Watson Research Center. She received the Ph.D degree from School of Computer Science, Carnegie Mellon University in 2010. Her research interests include rare cateogory analysis and heterogeneous machine learning with applications in social media analysis, semiconductor manufacturing, traffic prediction, etc. She has served on the organizing/program committees of many conferences, including ICML, NIPS, IJCAI, ICDM, SDM, etc.
Xia Ning (NEC Labs America)
Jianhui Chen (GE Global Research)
Jiayu Zhou (Arizona State University)
Shuiwang Ji (Old Dominion University)
Xinhua Zhang (National ICT Australia / NICTA)