Preprint
Aug 2026
NOMADD: Numerical Optimization of Models Adapting to Data Drift
This paper offers an alternative post-hoc method to reduce concept drift, which is applicable to a variety of models, from trees to neural networks to tabular foundation models, and explores the promise and challenges of extending this tool to other modalities.
Swapn Shah, K. Burghardt
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