Optimization

Optimization

8 Lectures · Dec 12, 2009

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/

Related categories

Uploaded videos:

video-img
49:35

Chordal Sparsity in Semidefinite Programming and Machine Learning

Lieven Vandenberghe

Jan 19, 2010

 · 

5745 Views

Invited Talk
video-img
22:06

A Pathwise Algorithm for Covariance Selection

Jan 19, 2010

 · 

4503 Views

Lecture
video-img
18:18

Active Set Algorithm for Structured Sparsity-Inducing Norm

Rodolphe Jenatton

Jan 19, 2010

 · 

5339 Views

Lecture
video-img
53:02

On Recent Trends in Extremely Large-Scale Convex Optimization

Arkadi Nemirovski

Jan 19, 2010

 · 

6884 Views

Invited Talk
video-img
17:12

Tree Based Ensemble Models Regularization by Convex Optimization

Bertrand Cornelusse

Jan 19, 2010

 · 

3901 Views

Lecture
video-img
20:21

On the Convergence of the Convex-Concave Procedure

Bharath K. Sriperumbudur

Jan 19, 2010

 · 

5237 Views

Lecture
video-img
22:16

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

Katya Scheinberg

Jan 19, 2010

 · 

4611 Views

Lecture
video-img
20:34

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

Ryota Tomioka

Jan 19, 2010

 · 

7454 Views

Lecture