Videos: ROKS 2013, International Workshop on Advances in Regularization, Optimization, Kernel Methods and Support Vector Machines

I was somehow led to believe that no video would be taken at ROKS 2013 ( International Workshop on Advances in Regularization, Optimization, Kernel Methods and Support Vector Machines). Well, I was wrong, it looks like the organizers contracted the videolectures folks, Here is the list of videos available from the workshop:, but first a big thank you to the organizing committee ( Johan Suykens, Andreas Argyriou, Kris De Brabanter, Moritz Diehl, Kristiaan Pelckmans, Marco Signoretto, Vanya Van Belle, Joos Vandewalle) for having the forethoughts of videotaping most of the talks. Enjoy!

Opening

• Welcome to ROKS 2013 Johan Suykens

Invited Talks

• Deep-er Kernels John Shawe-Taylor
• Connections between the Lasso and Support Vector Machines Martin Jaggi
• From Kernels to Causality Bernhard Schölkopf
• Beyond Stochastic Gradient Descent Francis R. Bach
• Domain Specific Languages for Convex Optimization Stephen P. Boyd
• Dynamic ℓ1 Reconstruction Justin Romberg
• Multi-task Learning Massimiliano Pontil
• Primal-Dual Subgradient Methods for Huge-Scale Problems Yurii Nesterov
• Living on the Edge - Phase Transitions in Random Convex Programs Joel Tropp
• Minimum Error Entropy Principle for Learning Ding-Xuan Zhou
• Learning from Weakly Labeled Data James Kwok

Oral session 1: Feature selection and sparsity

• The Graph-guided Group Lasso Zi Wang
• Feature Selection via Detecting Ineffective Features Kris De Brabanter

Oral session 2: Optimization algorithms

• The First-Order View of Boosting Methods: Computational Complexity and Connections to Regularization Paul Grigas
• Fixed-Size Pegasos for Large Scale Pinball Loss SVM Vilen Jumutc

Oral session 3: Kernel methods and support vector machines

• Output Kernel Learning Methods Francesco Dinuzzo
• Deep Support Vector Machines Marco Wiering
• Subspace Learning Alessandro Rudi
• Kernel Based Identification of Systems with Multiple Outputs Using Nuclear Norm Regularization Tillmann Falck

Oral session 4: Structured low-rank approximation

• Fast Algorithms for Informed Source Separation Augustin Lefèvre
• Structured Low-Rank Approximation as Optimization on a Grassmann Manifold Konstantin Usevich
• Scalable Structured Low Rank Matrix Optimization Problems Marco Signoretto

Oral session 5: Robustness

• Learning with Marginalized Corrupted Features Laurens van der Maaten
• Robust Near-Separable Nonnegative Matrix Factorization Using Linear Optimization Nicolas Gillis

Closing

• Closing Johan Suykens

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