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perturbation-analysis

topic1 events
papersSEP 10 04:00 UTC

Study proposes ensembling framework for quantifying algorithmic stability

A new machine learning preprint introduces a general framework for measuring how sensitive an algorithm is to perturbations of its input data, with the relevant notion of perturbation varying by setting. The authors tie this stability analysis to ensembling, indicating that combining multiple models can help make learning algorithms less sensitive to changes in the data.

machine-learning/theory/ensembling/stability