We invite high quality submissions for presentation (talks or poster presentations), or open problems during the workshop. We are especially interested in participants who can contribute theory / algorithms, applications, or implementations with a machine learning focus in the following areas:

* Stochastic, Parallel and Online Optimization:
o Large-scale learning, massive data sets
o Distributed algorithms
o Optimization on massively parallel architectures
o Optimization using GPUs, Streaming algorithms
o Decomposition for large-scale, message-passing and online learning
o Stochastic approximation
o Randomized algorithms
* Algorithms and Techniques (application oriented):
o Global and Lipschitz optimization
o Algorithms for non-smooth optimization
o Linear and higher-order relaxations
o Polyhedral combinatorics applications to ML problems
* Non-Convex Optimization:
o Non-convex quadratic programming, including binary QPs
o Convex Concave Decompositions, D.C. Programming, EM
o Training of deep architectures and large hidden variable models
o Approximation Algorithms
* Optimization with Sparsity constraints:
o Combinatorial methods for L0 norm minimization
o L1, Lasso, Group Lasso, sparse PCA, sparse Gaussians
o Rank minimization methods
o Feature and subspace selection
* Combinatorial Optimization:
o Optimization in Graphical Models
o Structure learning
o MAP estimation in continuous and discrete random fields
o Clustering and graph-partitioning
o Semi-supervised and multiple-instance learning

Submission Instructions

* The submissions should be ideally 4 pages long. Hard-limit: 6 pages.
* Open Problems may be of any length within the hard-limits
* The review process will be double-blind
* Please use the NIPS 2010 format for your submissions
* Submit at: http://www.easychair.org/conferences/?conf=opt2010