Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach


Jason Pacheco, Erik Sudderth ;
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:2200-2208, 2015.


Variants of max-product (MP) belief propagation effectively find modes of many complex graphical models, but are limited to discrete distributions. Diverse particle max-product (D-PMP) robustly approximates max-product updates in continuous MRFs using stochastically sampled particles, but previous work was specialized to tree-structured models. Motivated by the challenging problem of protein side chain prediction, we extend D-PMP in several key ways to create a generic MAP inference algorithm for loopy models. We define a modified diverse particle selection objective that is provably submodular, leading to an efficient greedy algorithm with rigorous optimality guarantees, and corresponding max-marginal error bounds. We further incorporate tree-reweighted variants of the MP algorithm to allow provable verification of global MAP recovery in many models. Our general-purpose Matlab library is applicable to a wide range of pairwise graphical models, and we validate our approach using optical flow benchmarks. We further demonstrate superior side chain prediction accuracy compared to baseline algorithms from the state-of-the-art Rosetta package.

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