13 citations · 13 across the 2 of their papers we have counts for
2 papers
cs.LG2024
Learned Feature Importance Scores for Automated Feature Engineering
Yihe Dong, Sercan Arik, Nathanael Yoder +1
Feature engineering has demonstrated substantial utility for many machine learning workflows, such as in the small data regime or when distribution shifts are severe. Thus automati…
cs.LG2022★ 13 cited
Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series
Sercan O. Arik, Nathanael C. Yoder, Tomas Pfister
Real-world time-series datasets often violate the assumptions of standard supervised learning for forecasting -- their distributions evolve over time, rendering the conventional tr…