OPT 2009: 2nd NIPS Workshop on Optimization for Machine Learning

1. Large Margin Classification with the Progressive Hedging Algorithm.    Boris Defourny; Louis Wehenkel
2. Bandit-Aided Boosting.    Robert Busa-Fekete; Balazs Kegl
3. A Pathwise Algorithm for Covariance Selection.    Vijay Krishnamurthy; Selin Ahipasaoglu; Alexandre d'Aspremont
4. Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparse Learning.    Ryota Tomioka; Taiji Suzuki; Masashi Sugiyama
5. Active Set Algorithm for Structured Sparsity-Inducing Norms.    Rodolphe Jenatton; Jean-Yves Audibert; Francis Bach
6. Mixed-Integer Support Vector Machine.    Wei Guan; Alexander Gray; Sven Leyffer
7. On the convergence of the concave-convex procedure.    Bharath Sriperumbudur; Gert Lanckriet
8. Tree based ensemble models regularization by convex optimization.    Bertrand Cornélusse; Pierre Geurts; Louis Wehenkel
9. Sampling-based optimization with mixtures.    Rémi Bardenet; Balazs Kegl
10. Variable Selection and Grouping with Multiple Graph Priors.    Marco Signoretto; Anneleen Daemen; Carlo Savorgnan; Johan Suykens
11. Feature Selection as a one-player game.    Romaric Gaudel; Michele Sebag
12. A D.C. Programming Approach to the Sparse Generalized Eigenvalue Problem.    Bharath Sriperumbudur; David Torres; Gert Lanckriet
13. SINCO - an Efficient Greedy Method for Learning Sparse INverse COvariance Matrix.    Katya Scheinberg; Irina Rish

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