  Optimization problems with an auxiliary latent variable structure in addition
to the main model parameters occur frequently in computer vision and machine
learning. The additional latent variables make the underlying optimization task
expensive, either in terms of memory (by maintaining the latent variables), or
in terms of runtime (repeated exact inference of latent variables). We aim to
remove the need to maintain the latent variables and propose two formally
justified methods, that dynamically adapt the required accuracy of latent
variable inference. These methods have applications in large scale robust
estimation and in learning energy-based models from labeled data.
