Conditional gradient algorithms for machine learning

We consider penalized formulations of machine learning problems with regularization penalty having conic structure. For several important learning problems, state-of-the-art optimization approaches such as proximal gradient algorithms are difficult to apply and computationally expensive, preventing from using them for large-scale learning purpose. We present a conditional gradient algorithm, with theoretical guarantees, and show promising experimental results on two largescale real-world datasets.