This event has ended. View the official site or create your own event → Check it out
This event has ended. Create your own
View analytic
Monday, December 7 • 19:00 - 23:59
Smooth and Strong: MAP Inference with Linear Convergence

Sign up or log in to save this to your schedule and see who's attending!

Maximum a-posteriori (MAP) inference is an important task for many applications. Although the standard formulation gives rise to a hard combinatorial optimization problem, several effective approximations have been proposed and studied in recent years. We focus on linear programming (LP) relaxations, which have achieved state-of-the-art performance in many applications. However, optimization of the resulting program is in general challenging due to non-smoothness and complex non-separable constraints.Therefore, in this work we study the benefits of augmenting the objective function of the relaxation with strong convexity. Specifically, we introduce strong convexity by adding a quadratic term to the LP relaxation objective. We provide theoretical guarantees for the resulting programs, bounding the difference between their optimal value and the original optimum. Further, we propose suitable optimization algorithms and analyze their convergence.

Monday December 7, 2015 19:00 - 23:59
210 C #78