Ensembling algorithms improve stability against data changes
Algorithmic stability via ensembling
Machine Learning
Summary
Algorithms sometimes change their outputs a lot if the input data is only a little different, which can be a problem. The authors developed a general way to understand how combining many versions of an algorithm (ensembling) by averaging can make the overall method more stable to any kind of data changes. They provide a mathematical guarantee related to how these combined parts vary together, giving clearer and better limits on stability than previous privacy-based approaches. Their framework also makes it easier to see why ensembling helps in practical situations.
What this means in practice
- •For machine learning engineers: Design ensemble models with theoretical stability guarantees against diverse data perturbations improving robustness in practical deployments.
- •For data security teams: Assess and enhance algorithm resilience to input changes beyond privacy-based methods using covariance analysis in ensemble methods.
A theory result. No direct application yet.
Authors
Rina Foygel Barber, Richard J. Samworth
Abstract
Algorithmic stability refers to the property of an algorithm being insensitive to perturbations of the input data, where the type of perturbation may vary depending on the setting. In this work, we develop a general framework to quantify the extent to which any ensembling strategy defined via averaging can yield stability guarantees for any type of data perturbation. Our main theoretical result is a guarantee on the stability of this ensembled algorithm, given in terms of the norm of a certain covariance operator that describes the ensembling process. We show how our general framework yields interpretable and intuitive insights in several examples of perturbations of practical interest, and provides much sharper guarantees than those obtained from privacy considerations.