Session B2 Statistics: Chair: Lukas Sommeregger
- Felix Benning (Universität Mannheim)
- Maximilian Steffen ()
- Jan Rabe (Universität Hamburg)
Increasing computational power and storage capacity have made high-dimensional datasets accessible to many areas of research such as medicine, natural and social sciences. While classical statistical methods are not compatible with high-dimensional data, especially due to the curse of dimensionality, machine learning methods have been successfully applied to regression problems in practice. On...
While gradient descent is ubiquitous in Machine Learning, there is no adaptive way to select a learning rate yet. This forces practitioners to do "hyperparameter tuning". We review how optimization schemes can be motivated using Taylor approximations and develop intuition why this results in unknown hyperparameters. We then replace the Taylor approximation with a statistical Best Linear...
Random forests are a popular method in supervised learning and can be
used for regression and classification problems. For a regression problem
a random forest averages the results of several randomized decision trees
that are constructed on different subsamples of the dataset. In practice
random forests appear to be very successful and are therefore a commonly
used algorithm. Contrary to...