Guest Editorial Special Issue on Discriminative Learning for Model Optimization and Statistical Inference

Model optimization and statistical inference have played a central role in various applications of computational intelligence, data analytics, and computer vision. Traditional approaches are usually based on model-centric learning. That is, even after model training, it is still required to design proper algorithms and to specify hand-crafted parameters for optimization and inference. Recently, discriminative learning has demonstrated its power for process-centric learning. Taking domain expertise and problem structure into account, problem-specific deep architectures can be formed by unfolding the model inference as an iterative process, and the parameters of the optimization process can then be learned from training data. These solutions are closely related with bilevel optimization, partial differential equation (PDE), as well as meta learning, and can provide new insights into the studies of versatile statistical and …