Continuous Optimization Seminar - Yaoliang Yu

Wednesday, December 13, 2017 4:00 pm - 4:00 pm EST (GMT -05:00)

Title: Generalized Conditional Gradient for Sparse Estimation

Speaker: Yaoliang Yu  
Affiliation: University of Waterloo
Room: MC 5479


We will be discussing the paper (bearing the same title) of Yaoliang Yu, Xinhua Zhang, and Dale Schuurmans.  Structured sparsity is an important modelling tool that expands the applicability of convex formulations for data analysis, however it also creates significant challenges for efficient algorithm design. In this paper we investigate the generalized conditional gradient (GCG) algorithm for solving structured sparse optimization problems—demonstrating that, with some enhancements, it can provide a more efficient alternative to current state of the art approaches. After providing a comprehensive overview of the convergence properties of GCG, we develop efficient methods for evaluating polar operators, a subroutine that is required in each GCG iteration. In particular, we show how the polar operator can be efficiently evaluated in two important scenarios: dictionary learning and structured sparse estimation. A further improvement is achieved by interleaving GCG with fixed-rank local subspace optimization. A series of experiments on matrix completion, multi-class classification, multi-view dictionary learning and overlapping group lasso shows that the proposed method can significantly reduce the training cost of current alternatives.