Call for Papers
Discrete Optimization in Machine Learning
Structures, Algorithms and Applications
Workshop at the
24th Annual Conference on Neural Information Processing Systems (http://nips.cc/)
(NIPS 2010)
Submission Deadline: Friday October 29, 2010
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Solving optimization problems with ultimately discretely solutions is becoming increasingly important in machine learning: At the core of statistical machine learning is to infer conclusions from data, and when the variables underlying the data are discrete, both the tasks of inferring the model from data, as well as performing predictions using the estimated model are discrete optimization problems. This workshop aims at exploring discrete structures relevant to machine learning and
techniques relevant to solving discrete learning problems. In addition to studying discrete structures and algorithms, this year's workshop will put a particular emphasis on novel applications of discrete optimization in machine learning.
We would like to encourage high quality submissions of short papers relevant to the workshop topics. Accepted papers will be presented as spotlight talks and posters. Of particular interest are new algorithms with theoretical guarantees, as well as applications of discrete optimization to machine learning problems in areas such as
the following:
Combinatorial algorithms
- Submodular & supermodular optimization
- Discrete convex analysis
- Pseudo-boolean optimization
- Randomized / approximation algorithms
Continuous relaxations
- Sparse approximation & compressive sensing
- Regularization techniques
- Structured sparsity models
Applications
- Graphical model inference & structure learning
- Clustering
- Feature selection, active learning & experimental design
- Structured prediction
- Novel discrete optimization problems in ML
Submission deadline: October 29, 2010
Length & Format: max. 6 pages NIPS 2010 format
Time & Location: December 11 2010, Whistler, Canada
Submission instructions: Email to submit@discml.cc
Organizers: Andreas Krause (California Institute of Technology),
Pradeep Ravikumar (University of Texas, Austin),
Jeff A. Bilmes (University of Washington),
Stefanie Jegelka (Max Planck Institute for Biological Cybernetics in Tuebingen, Germany)
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