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cs.LG2025
VirnyFlow: Optimizing ML Pipelines for Accuracy, Fairness, and Stability at Scale
Denys Herasymuk, Anastasiia Mozghova, Nazar Protsiv +2
Developing machine learning (ML) systems for real-world deployment requires navigating context-dependent trade-offs among accuracy, fairness, stability, and other objectives. Exist…
cs.LG2023★ 2 cited
An Epistemic and Aleatoric Decomposition of Arbitrariness to Constrain the Set of Good Models
Falaah Arif Khan, Denys Herasymuk, Nazar Protsiv +1
Recent research reveals that machine learning (ML) models are highly sensitive to minor changes in their training procedure, such as the inclusion or exclusion of a single data poi…