About
One area of high impact both in theory and applications is kernel methods and support vector machines. Optimization problems, learning and representations of models are key ingredients in these methods. On the other hand considerable progress has also been made on regularization of parametric models, including methods for compressed sensing and sparsity, where convex optimization plays a prominent role. The aim of ROKS-2013 is to provide a multi-disciplinary forum where researchers of different communities can meet, to find new synergies along these areas, both at the level of theory and applications.
The scope includes but is not limited to: *Regularization: L2, L1, Lp, lasso, group lasso, elastic net, spectral regularization, nuclear norm, others *Support vector machines, least squares support vector machines, kernel methods, gaussian processes and graphical models *Lagrange duality, Fenchel duality, estimation in Hilbert spaces, reproducing kernel Hilbert spaces, Banach spaces, operator splitting *Optimization formulations, optimization algorithms *Supervised, unsupervised, semi-supervised learning, inductive and transductive learning *Multi-task learning, multiple kernel learning, choice of kernel functions, manifold learning *Prior knowledge incorporation *Approximation theory, learning theory, statistics *Matrix and tensor completion, learning with tensors *Feature selection, structure detection, regularization paths, model selection *Sparsity and interpretability *On-line learning and optimization *Applications in machine learning, computational intelligence, pattern analysis, system identification, signal processing, networks, datamining, others *Software
For more information visit the Workshop´s website.
Videos
Opening

Welcome to ROKS 2013
Aug 26, 2013
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3832 views
Invited Talks

Connections between the Lasso and Support Vector Machines
Aug 26, 2013
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6611 views

Multi-task Learning
Aug 26, 2013
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8047 views

Primal-Dual Subgradient Methods for Huge-Scale Problems
Aug 26, 2013
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5895 views

Deep-er Kernels
Aug 26, 2013
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12003 views

From Kernels to Causality
Aug 26, 2013
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5859 views

Living on the Edge - Phase Transitions in Random Convex Programs
Aug 26, 2013
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5391 views

Domain Specific Languages for Convex Optimization
Aug 26, 2013
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5670 views

Learning from Weakly Labeled Data
Aug 26, 2013
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5377 views

Dynamic ℓ1 Reconstruction
Aug 26, 2013
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4513 views

Beyond Stochastic Gradient Descent
Aug 26, 2013
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7149 views

Minimum Error Entropy Principle for Learning
Aug 26, 2013
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4414 views
Oral session 1: Feature selection and sparsity

The Graph-guided Group Lasso
Aug 26, 2013
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4049 views

Feature Selection via Detecting Ineffective Features
Aug 26, 2013
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3772 views
Oral session 2: Optimization algorithms

Fixed-Size Pegasos for Large Scale Pinball Loss SVM
Aug 26, 2013
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3272 views

The First-Order View of Boosting Methods: Computational Complexity and Connectio...
Aug 26, 2013
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3245 views
Oral session 3: Kernel methods and support vector machines

Kernel Based Identification of Systems with Multiple Outputs Using Nuclear Norm ...
Aug 26, 2013
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3194 views

Subspace Learning
Aug 26, 2013
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5494 views

Output Kernel Learning Methods
Aug 26, 2013
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4423 views

Deep Support Vector Machines
Aug 26, 2013
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21398 views
Oral session 4: Structured low-rank approximation

Fast Algorithms for Informed Source Separation
Aug 26, 2013
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3118 views

Scalable Structured Low Rank Matrix Optimization Problems
Aug 26, 2013
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3496 views

Structured Low-Rank Approximation as Optimization on a Grassmann Manifold
Aug 26, 2013
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4490 views
Oral session 5: Robustness

Learning with Marginalized Corrupted Features
Aug 26, 2013
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4728 views

Robust Near-Separable Nonnegative Matrix Factorization Using Linear Optimization
Aug 26, 2013
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5601 views
Closing

Closing
Aug 26, 2013
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2644 views