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AISB event Bulletin Item

CFP: ICML 07 Workshop on Constrained Optimization and Structured Output Spaces

Call for Papers:

ICML Workshop on Constrained Optimization
and Structured Output Spaces

A large amount of machine learning research in recent years has focused on domains with structured output spaces, such as protein secondary structure prediction, natural language parsing, and various applications in computer vision and information retrieval.  Graphical models provide powerful tools to utilize structure in these domains.  In addition, recent work has focused on the use of standard supervised learning techniques to solve these types of problems.

At the same time, mathematical programming in machine learning continues to be a focus of intense research.  New applications and uses for support vector machines continue to be discovered.  Integer linear programming has worked its way into many interesting learning and inference algorithms. New techniques for approximate constrained optimization have opened up the possibility of solving optimizations with exponential numbers of constraints.

To discuss the increasing cross-pollination of these two areas, ICML 2007 is hosting a workshop on Constrained Optimization and Structured Output Spaces.  For this workshop we invite submission of papers on original research in the areas of constrained optimization and learning in structured output spaces.  Submissions that address the following questions are particularly sought after:

Overview of Titular Areas:  There is a great deal of work on the intersection of these two areas.  What are good examples of the state of the art in these areas?  On what domains/types of problems has this convergence been successful?  On what types of problems has it failed and why?  When it does fail, what other methods succeed?

Generality of Techniques:  Some of the applications used in the literature require a great deal of specialization of the candidate optimization technique.  To what extent can these specialized techniques be generalized to solve other, seemingly unrelated problems?  On what types of problems will the candidate technique fail even if it can be applied readily?

Heuristic vs. Exact Optimization:  Many of the techniques used for exact constrained optimization are still extremely time consuming and often impractical.  When can heuristic approaches be used to give approximate solutions to these optimizations and when are they appropriate?  What heuristic techniques are available?  What is the trade-off between heuristic solutions and exact ones in a given domain?

Applications:  What are some large-scale, real-world applications on which these techniques have been applied effectively?  What are some as yet untested applications, preferably with publicly available datasets, that show promise?

Theory:  What theoretical tools are available to categorize these problems and analyze the solution techniques? How do the different approaches to solving these problems relate to each other?


Submissions should be no longer than six pages in length, and should follow the ICML submission style guidelines. Identifying information including names of authors, affiliations, and contact information should appear on the first page of the paper. Authors of accepted papers will be invited to give a presentation of the work at the workshop and final papers will be published electronically on the workshop web site.

Please go to:

for more information or to submit a paper.

Important Dates

Submissions Due:  23 April 2007
Notification of Acceptance:  14 May 2007
Camera-ready Papers Due:  28 May 2007
Workshop Date:  24 June 2007

Organizing Committee

Thomas Dietterich - Oregon State University
Charles Parker - Oregon State University
Eric Xing - Carnegie Mellon University
Scott Yih - Microsoft Research

Program Committee

Ulf Brefeld, Humboldt-Universitt zu Berlin
Thomas Dietterich, Oregon State University
Lise Getoor, University of Maryland
Rong Jin, Michigan State University
Thorsten Joachims, Cornell University
Charles Parker, Oregon State University
Dan Roth, University of Illinois, Champaign-Urbana
Prasad Tadepalli, Oregon State University
Eric Xing, Carnegie Mellon University
Scott Yih, Microsoft Research