Difference between revisions of "OPT 2008"

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  | Acronym = OPT 2008
 
  | Acronym = OPT 2008
 
  | Title = Optimization for Machine Learning (NIPS Workshop 2008)
 
  | Title = Optimization for Machine Learning (NIPS Workshop 2008)
  | Type = Conference
+
  | Type = Workshop
 
  | Field = Machine learning
 
  | Field = Machine learning
 
  | Homepage = opt2008.kyb.tuebingen.mpg.de/
 
  | Homepage = opt2008.kyb.tuebingen.mpg.de/
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<pre>
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We invite high quality submissions for presentation as talks or posters  during the workshop. We are especially interested in participants who can contribute in the following areas:
We invite high quality submissions for presentation as talks or posters  during the workshop. We are especially interested in participants who can contribute in the following areas:
 
  
    * Non-Convex Optimization
+
* Non-Convex Optimization example problems in ML include
      example problems in ML include
+
** Problems with sparsity constraints
          o Problems with sparsity constraints
+
** Sparse PCA
          o Sparse PCA
+
** Non-negative matrix and tensor approximation
          o Non-negative matrix and tensor approximation
+
** Non-convex quadratic programming
          o Non-convex quadratic programming
+
* Combinatorial and Discrete Optimization example problems in ML include
    * Combinatorial and Discrete Optimization
+
** Estimating MAP solutions to discrete random fields
      example problems in ML include
+
** Clustering and graph-partitioning
          o Estimating MAP solutions to discrete random fields
+
** Semi-supervised and multiple-instance learning
          o Clustering and graph-partitioning
+
** Feature and subspace selection
          o Semi-supervised and multiple-instance learning
+
* Stochastic, Parallel and Online Optimization example problems in ML include
          o Feature and subspace selection
+
** Massive data sets
    * Stochastic, Parallel and Online Optimization
+
** Distributed learning algorithms
      example problems in ML include
+
* Algorithms and Techniques especially with a focus on an underlying application
          o Massive data sets
+
** Polyhedral combinatorics, polytopes and strong valid inequalities
          o Distributed learning algorithms
+
** Linear and higher-order relaxations
    * Algorithms and Techniques
+
** Semidefinite programming relaxations
      especially with a focus on an underlying application
+
** Decomposition for large-scale, message-passing and online learning
          o Polyhedral combinatorics, polytopes and strong valid inequalities
+
** Global and Lipschitz optimization
          o Linear and higher-order relaxations
+
** Algorithms for non-smooth optimization
          o Semidefinite programming relaxations
+
** Approximation Algorithms
          o Decomposition for large-scale, message-passing and online learning
 
          o Global and Lipschitz optimization
 
          o Algorithms for non-smooth optimization
 
          o Approximation Algorithms
 
  
 
The above list is not exhaustive, and we welcome submissions on highly related topics too.
 
The above list is not exhaustive, and we welcome submissions on highly related topics too.
  
    * Deadline for submission of papers: 17th October 2008
+
* Deadline for submission of papers: 17th October 2008
    * Notification of acceptance: 7th November 2008
+
* Notification of acceptance: 7th November 2008
    * Final version of submission: 20th November 2008
+
* Final version of submission: 20th November 2008
    * Workshop date: 12th or 13th December 2008
+
* Workshop date: 12th or 13th December 2008
</pre>This CfP was obtained from [http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=3741&amp;copyownerid=2 WikiCFP]
 

Latest revision as of 20:28, 14 October 2008

OPT 2008
Optimization for Machine Learning (NIPS Workshop 2008)
Dates Dec 12, 2008 (iCal) - Dec 13, 2008
Homepage: opt2008.kyb.tuebingen.mpg.de/
Location
Location: Whistler, Canada
Loading map...

Important dates
Submissions: Oct 17, 2008
Notification: Nov 7, 2008
Camera ready due: Nov 20, 2008
Table of Contents


We invite high quality submissions for presentation as talks or posters during the workshop. We are especially interested in participants who can contribute in the following areas:

  • Non-Convex Optimization example problems in ML include
    • Problems with sparsity constraints
    • Sparse PCA
    • Non-negative matrix and tensor approximation
    • Non-convex quadratic programming
  • Combinatorial and Discrete Optimization example problems in ML include
    • Estimating MAP solutions to discrete random fields
    • Clustering and graph-partitioning
    • Semi-supervised and multiple-instance learning
    • Feature and subspace selection
  • Stochastic, Parallel and Online Optimization example problems in ML include
    • Massive data sets
    • Distributed learning algorithms
  • Algorithms and Techniques especially with a focus on an underlying application
    • Polyhedral combinatorics, polytopes and strong valid inequalities
    • Linear and higher-order relaxations
    • Semidefinite programming relaxations
    • Decomposition for large-scale, message-passing and online learning
    • Global and Lipschitz optimization
    • Algorithms for non-smooth optimization
    • Approximation Algorithms

The above list is not exhaustive, and we welcome submissions on highly related topics too.

  • Deadline for submission of papers: 17th October 2008
  • Notification of acceptance: 7th November 2008
  • Final version of submission: 20th November 2008
  • Workshop date: 12th or 13th December 2008
Facts about "OPT 2008"
AcronymOPT 2008 +
Camera ready dueNovember 20, 2008 +
End dateDecember 13, 2008 +
Event typeConference +
Has coordinates50° 7' 2", -122° 57' 15"Latitude: 50.117191666667
Longitude: -122.95430277778
+
Has location cityWhistler +
Has location countryCategory:Canada +
Homepagehttp://opt2008.kyb.tuebingen.mpg.de/ +
IsAEvent +
NotificationNovember 7, 2008 +
Start dateDecember 12, 2008 +
Submission deadlineOctober 17, 2008 +
TitleOptimization for Machine Learning (NIPS Workshop 2008) +