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ComplexOutputs 2014 - 2014 Workshop: Representation and Learning Methods for Complex Outputs



VenueQuebec, Canada Canada



Topics/Call fo Papers

NIPS 2014 Workshop: Representation and Learning Methods for Complex Outputs
December 12 , 2014
Montreal, Quebec, Canada
Learning problems that involve complex outputs are becoming increasingly prevalent in machine learning research. For example, work on image and document tagging now considers thousands of labels chosen from an open vocabulary, with only partially labeled instances available for training. Given limited labeled data, these settings also create zero-shot learning problems with respect to omitted tags, leading to the challenge of inducing semantic label representations. Furthermore, prediction targets are often abstractions that are difficult to predict from raw input data, but can be better predicted from learned latent representations. Finally, when labels exhibit complex inter-relationships it is imperative to capture latent label relatedness to improve generalization.
Although representation learning has already achieved state of the art results in standard settings, recent research has begun to explore the use of learned representations in more complex scenarios, such as structured output prediction, multiple modality co-embedding, multi-label prediction, and zero-shot learning. These emerging research topics however have been conducted in separate sub-areas, without proper connections drawn between similar ideas, hence general methods and understanding have not yet emerged from the disconnected pursuits. This workshop will bring together separate communities that have been working on novel representation and learning methods for problems with complex outputs.
The aim of this workshop is to identify fundamental strategies, highlight differences, and identify the prospects for developing a set of systematic theory and methods for learning problems with complex outputs. The target communities include researchers working on image tagging, document categorization, natural language processing, large vocabulary speech recognition, deep learning, latent variable modeling, and large scale multi-label learning. Relevant topics include, but are not limited to, the following:
* Multi-label learning with large and/or incomplete output spaces
* Zero-shot learning
* Co-embedding
* Learning output kernels
* Output structure learning
We invite submissions in NIPS 2014 format with a maximum of 4 pages, excluding references. Anonymity is not required. Relevant works that have been recently published or presented elsewhere are allowed, provided that previous publications are explicitly acknowledged. Please submit papers in PDF format at
Important Dates:
Submission Deadline: October 10, 2014
Author Notification: October 26, 2014
Workshop: December 12 or 13, 2014
Invited Speakers:
[Confirmed] Hal Daume III, University of Maryland
[Confirmed] Francesco Dinuzzo, IBM Research, Dublin
[Tentative] Julia Hockenmaier, University of Illinois at Urbana-Champaign
[Confirmed] Honglak Lee, University of Michigan
[Tentative] Fei-Fei Li, Stanford University
[Tentative] Noah Smith, Carnegie Mellon University
[Tentative] Rich Sutton, University of Alberta
[Tentative] Jieping Ye, Arizona State University
Yuhong Guo, Temple University
Dale Schuurmans, University of Alberta
Kilian Q. Weinberger, Washington University
Richard Zemel, University of Toronto
The organizers can be contacted at

Last modified: 2014-08-29 22:03:44