Automated Algorithm Selection: Predict which algorithm to use!
by Marius Lindauer
Abstract: To achieve state-of-the-art performance, it is often crucial to select a suitable algorithm for a given problem instance. For example, what is the best search algorithm for a given instance of a search problem; or what is the best machine learning algorithm for a given dataset? By trying out many different algorithms on many problem instances, developers learn an intuitive mapping from some characteristics of a given problem instance (e.g., the number of features of a dataset) to a well-performing algorithm (e.g., random forest). The goal of automated algorithm selection is to learn from data, how to automatically select a well-performing algorithm given such characteristics. In this talk, I will give an overview of the key ideas behind algorithm selection and different approaches addressing this problem by using machine learning.
To achieve state-of-the-art performance in machine learning (ML), it is very important to choose the right algorithm and its hyperparameters for a given dataset. Since finding the correct settings needs a lot of time and expert knowledge, we developed AutoML tools that can be used out-of-the-box with minimal expertise in machine learning. In this session, I will present two state-of-the-art tools in this field: (i) auto-sklearn (www.automl.org/auto-sklearn/) for classical machine learning and (ii) AutoPyTorch (www.automl.org/autopytorch/) for deep learning.