#### About

**Optimization for Machine Learning**

## It is fair to say that at the heart of every machine learning algorithm is an optimization problem. It is only recently that this viewpoint has gained significant following. Classical optimization techniques based on convex optimization have occupied center-stage due to their attractive theoretical properties. But, new non-smooth and non-convex problems are being posed by machine learning paradigms such as structured learning and semi-supervised learning. Moreover, machine learning is now very important for real-world problems which often have massive datasets, streaming inputs, and complex models that also pose significant algorithmic and engineering challenges. In summary, machine learning not only provides interesting applications but also challenges the underlying assumptions of most existing optimization algorithms. Therefore, there is a pressing need for optimization "tuned" to the machine learning context. For example, techniques such as non-convex optimization (for semi-supervised learning), combinatorial optimization and relaxations (structured learning), non-smooth optimization (sparsity constraints, L1, Lasso, structure learning), stochastic optimization (massive datasets, noisy data), decomposition techniques (parallel and distributed computation), and online learning (streaming inputs) are relevant in this setting. These techniques naturally draw inspiration from other fields, such as operations research, theoretical computer science, and the optimization community. Motivated by these concerns, we would like to address these issues in the framework of this workshop.

## The Workshop homepage can be found at http://opt.kyb.tuebingen.mpg.de/

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## Uploaded videos:

49:35

### Chordal Sparsity in Semidefinite Programming and Machine Learning

Jan 19, 2010

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5742 Views

22:06

### A Pathwise Algorithm for Covariance Selection

Jan 19, 2010

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4500 Views

18:18

### Active Set Algorithm for Structured Sparsity-Inducing Norm

Jan 19, 2010

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5336 Views

53:02

### On Recent Trends in Extremely Large-Scale Convex Optimization

Jan 19, 2010

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6882 Views

17:12

### Tree Based Ensemble Models Regularization by Convex Optimization

Jan 19, 2010

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3897 Views

20:21

### On the Convergence of the Convex-Concave Procedure

Jan 19, 2010

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5236 Views

22:16

### SINCO - An Efficient Greedy Method for Learning Sparse INverse COvariance Matrix

Jan 19, 2010

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4610 Views

20:34

### Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparse Learn...

Jan 19, 2010

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7451 Views