o    Overview, Format and Audience

o    Paper Submission

o    Invited Speakers

o    Workshop References

o    Previous Edition of the Workshop

o    Organizers

SDM 2015 Workshop on Heterogeneous Learning (Full Day)


Overview by Organizers


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.

Format:

The proposed workshop will have keynote talks, invited talks, oral/poster presentations from paper authors, and panel/open discussions.

Target Audience:

Researchers and practitioners working on problems exhibiting multiple types of heterogeneity.


Paper Submission Return to Top


 

Key Dates

 

03/10/2015:     Paper Submission

03/31/2015:     Author Notification

04/10/2015:     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 sdm15hl@gmail.com by 11:59pm MDT, March 10, 2015.

 


Invited Speakers Return to Top


 

Heng Wang (University of Texas, Arlington)

 

Yan Liu (University of Southern California)

 

Zhi-Hua Zhou (Nanjing University, China)

 

Qiang Yang (Hong Kong University of Science and Technology)

 

Ruslan Salakhutdinov (University of Toronto)

 

Dinggang Shen (UNC)

 

Li Shen (Indiana University School of Medicine)

 

Hanchuan Peng (Allen Institute for Brain Science)

 

Fei Sha (University of Southern California)

 

Shai Ben-David (University of Waterloo)

 


Workshop References Return to Top


 

The proposed workshop builds upon the previous edition at SDM 2014 [1], as well as other workshops on transfer learning/domain adaptation [2,3,4,5], crowdsourcing [6,7,8]. The major difference is that, while previous workshops focused on a single type of heterogeneity, the proposed workshop has a broader scope, and encourages submissions on modeling both a single and multiple types of heterogeneities.

 

Reference

 

[1] SDM 2014 Workshop on Heterogeneous Learning

[2] ICML 2013 Workshop on Theoretically Grounded Transfer Learning

 

[3] NIPS 2011 Workshop on Challenges in Learning Hierarchical Models: Transfer Learning and Optimization

 

[4] NIPS 2011 Domain Adaptation Workshop: Theory and Application

 

[5] NIPS 2010 Workshop on Transfer Learning Via Rich Generative Models

 

[6] ICML 2013 Workshop on Machine Learning Meets Crowdsourcing

 

[7] ICML 2012 Workshop on Machine Learning in Human Computation & Crowdsourcing

 

[8] NIPS 2012 Workshop on Human Computation for Science and Computational Sustainability

 


Previous Edition of the Workshop Return to Top


 

We organized this workshop at SDM 2014. It consists of 3 keynote talks (1 hour each), 1 invited talk and 4 paper presentations (half an hour each), and an open floor discussion (45 minutes). It was well attended throughout the day with around 25 people.

 

 


Organizers Return to Top


 

Organizing Committee

  • Jieping Ye (Arizona State University): jieping.ye@asu.edu

Jieping Ye (jieping.ye@asu.edu) 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, the KDD best research paper runner up in 2013, and the KDD best student paper award in 2014.

  • Yuhong Guo (Temple University): yuhong@temple.edu

Yuhong Guo (yuhong@temple.edu) 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 (Arizona State University): jingrui.he@asu.edu

Jingrui He (jingrui.he@asu.edu) is an Assistant Professor in the School of Computing, Informatics, Decision Systems Engineering at Arizona State University. She received the Ph.D degree from School of Computer Science, Carnegie Mellon University in 2010. Her research interests include rare category analysis and heterogeneous machine learning with applications in social media analysis, semiconductor manufacturing, traffic prediction, medical informatics, etc. She has served on the organizing/senior program committees of many conferences, including ICML, KDD, IJCAI, ICDM, SDM, etc.

 

Program Committee

  • 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)
  • Pei Yang (The City University of New York)