Valid Inference for Machine Learning Model Parameters
This provides a method for assessing parameter uncertainty to improve model generalization, addressing a core issue in machine learning inference.
The paper tackles the problem of overtraining in machine learning by constructing valid confidence sets for the optimal model parameters using only training data, and demonstrates that bootstrapping techniques can approximate these confidence sets effectively.
The parameters of a machine learning model are typically learned by minimizing a loss function on a set of training data. However, this can come with the risk of overtraining; in order for the model to generalize well, it is of great importance that we are able to find the optimal parameter for the model on the entire population -- not only on the given training sample. In this paper, we construct valid confidence sets for this optimal parameter of a machine learning model, which can be generated using only the training data without any knowledge of the population. We then show that studying the distribution of this confidence set allows us to assign a notion of confidence to arbitrary regions of the parameter space, and we demonstrate that this distribution can be well-approximated using bootstrapping techniques.