Provable Variational Inference for Constrained Log-Submodular Models

Josip Djolonga, Stefanie Jegelka, Andreas Krause

Neural Information Processing Systems 

In this work, we undertake a variational inference approach and approximate these rich distributions with simpler ones that respect the combinatorial constraints but are fully tractable. These approximations posses very strong negativeassociation properties, which we utilize inour theory.