#### About

Fortunately, most discrete optimization problems that arise in machine learning have specific structure, which can be leveraged in order to develop tractable exact or approximate optimization procedures. For example, consider the case of a discrete graphical model over a set of random variables. For the task of prediction, a key structural object is the "marginal polytope," a convex bounded set characterized by the underlying graph of the graphical model. Properties of this polytope, as well as its approximations, have been successfully used to develop efficient algorithms for inference. For the task of model selection, a key structural object is the discrete graph itself. Another problem structure is sparsity: While estimating a high-dimensional model for regression from a limited amount of data is typically an ill-posed problem, it becomes solvable if it is known that many of the coefficients are zero. Another problem structure, submodularity, a discrete analog of convexity, has been shown to arise in many machine learning problems, including structure learning of probabilistic models, variable selection and clustering. One of the primary goals of this workshop is to investigate how to leverage such structures.

The focus of this year´s workshop is on the interplay between discrete optimization and machine learning: How can we solve inference problems arising in machine learning using discrete optimization? How can one solve discrete optimization problems involving parameters that themselves are estimated from training data? How can we solve challenging sequential and adaptive discrete optimization problems where we have the opportunity to incorporate feedback (online and active learning with combinatorial decision spaces)? We will also explore applications of such approaches in computer vision, NLP, information retrieval etc.

Workshop homepage: http://discml.cc/

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## Uploaded videos:

## Keynote Talk

### Introduction to Jack Edmonds´s talk

Jan 25, 2012

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4633 Views

### Polymatroids and Submodularity

Jan 25, 2012

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8948 Views

## Invited Talks

### Exploiting Problem Structure for Efficient Discrete Optimization

Jan 25, 2012

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5571 Views

### Learning with Submodular Functions: A Convex Optimization Perspective

Jan 25, 2012

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7616 Views

### Combinatorial prediction games

Jan 25, 2012

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4352 Views