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cs.LG2026
NOMADD: Numerical Optimization of Models Adapting to Data Drift
Swapn Shah, Keith Burghardt
Tabular model performance degrades when feature distributions change over time or the relationship between features and outcome variables change over time, known as data drift and…
cs.LG2026
FAMOSE: A ReAct Approach to Automated Feature Discovery
Keith Burghardt, Jienan Liu, Sadman Sakib +2
Feature engineering remains a critical yet challenging bottleneck in machine learning, particularly for tabular data, as identifying optimal features from an exponentially large fe…