Talks and Poster Presentations (with Proceedings-Entry):
J. Jancsary, G. Matz, H. Trost:
"An Incremental Subgradient Algorithm for Approximate MAP Estimation in Graphical Models";
Talk: NIPS International Workshop on Optimization for Machine Learning,
12-10-2010; in: "Proc. 3rd International Workshop on Optimization for Machine Learning",
We present an incremental subgradient algorithm for approximate computation of maximum-a-posteriori (MAP) states in cyclic graphical models. Its most strik- ing property is its immense simplicity: each iteration requires only the solution of a sequence of trivial optimization problems. The algorithm can be equally un- derstood as a degenerated dual decomposition scheme or as minimization of a degenerated tree-reweighted upper bound and assumes a form that is reminiscent of message-passing. Despite (or due to) its conceptual simplicity, it is equipped with important theoretical guarantees and exposes strong empirical performance.
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.