Power transform revisited: numerically stable, and federated

Conference item

Power transforms are popular parametric methods for making data more Gaussian-like, and are widely used as preprocessing steps in statistical analysis and machine learning. However, we find that direct implementations of power transforms suffer from severe numerical instabilities, which can lead to incorrect results or even crashes. In this paper, we provide a comprehensive analysis of the sources of these instabilities and propose effective remedies. We further extend power transforms to the federated learning setting, addressing both numerical and distributional challenges that arise in this context. Experiments on real-world datasets demonstrate that our methods are both effective and robust, substantially improving stability compared to existing approaches.

Authors: Xu, X; Cormode, G

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: OpenReview
• Article number: 1247
• Publication date: 2026-02-03
• Acceptance date: 2026-01-22
• Event title: 29th International Conference on Artificial Intelligence and Statistics (AISTATS 2026)
• Event location: Tangier, Morocco
• Event website: https://virtual.aistats.org/Conferences/2026
• Event start date: 2026-05-02
• Event end date: 2026-05-05
• Language: English