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