Dear all,
Our next reading group is scheduled as follows:
Paper: Why does Throwing Away Data Improve Worst-Group Error? [1] Presenter: Jake Fawkes [2] Date/Time: Wednesday 29.11.2023, 14:30 - 16:00 Location: CISPA C0 Building: 0.01
Abstract: When facing data with imbalanced classes or groups, practitioners follow an intriguing strategy to achieve best results. They throw away examples until the classes or groups are balanced in size, and then perform empirical risk minimization on the reduced training set. This opposes common wisdom in learning theory, where the expected error is supposed to decrease as the dataset grows in size. In this work, we leverage extreme value theory to address this apparent contradiction. Our results show that the tails of the data distribution play an important role in determining the worst-group-accuracy of linear classifiers. When learning on data with heavy tails, throwing away data restores the geometric symmetry of the resulting classifier, and therefore improves its worst-group generalization.
For more details about future arrangements, please see this sheet [here [3]].
Best Regards, Kiet Vo
Links: ------ [1] https://arxiv.org/abs/2205.11672 [2] https://csml.stats.ox.ac.uk/people/fawkes/ [3] https://docs.google.com/spreadsheets/d/1vtgEezBqS4d_ACPt-emK2NT52x7nofX9jxgH...