When training a learning machine, both practical and theoretical considerations may yield to split the problem into multiple levels of inference. Typically, at the lower level, the parameters of individual models are optimized and at the second level the best model is selected, e.g. via cross-validation. But, there may be more than two levels of inference and cross-validation is not the only way of addressing the resulting optimization problem. This workshop will revisit the problem of model selection, with the goal of bridging the gap between theory and practice. A * game of model selection is organized *, check the web-site!