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Optimization problems with an auxiliary latent variable structure in additionto the main model parameters occur frequently in computer vision and machinelearning. The additional latent variables make the underlying optimization taskexpensive, either in terms of memory (by maintaining the latent variables), orin terms of runtime (repeated exact inference of latent variables). We aim toremove the need to maintain the latent variables and propose two formallyjustified methods, that dynamically adapt the required accuracy of latentvariable inference. These methods have applications in large scale robustestimation and in learning energy-based models from labeled data.

Truncated Inference for Latent Variable Optimization Problems: Application to Robust Estimation and Learning