Greedy algorithms and projection-free first-order optimization algorithms are at the core of many of the state of the art sparse methods in machine learning, signal processing, harmonic analysis, statistics and other seemingly unrelated areas, with different goals at first sight. Examples include matching pursuit, boosting, greedy methods for sub-modular optimization, with applications ranging from large-scale structured prediction to recommender systems. In the field of optimization, the recent renewed interest in Frank-Wolfe/conditional gradient algorithms opens up an interesting perspective towards a unified understanding of these methods, with a big potential to translate the rich existing knowledge about the respective greedy methods between the different fields.

The goal of this workshop is to take a step towards building a modern and consistent perspective on these related algorithms. The workshop will gather renowned experts working on those algorithms in machine learning, optimization, signal processing, statistics and harmonic analysis, in order to engender a fruitful exchange of ideas and discussions and to push further the boundaries of scalable and efficient optimization for learning problems.