110 citations · 185 across the 21 of their papers we have counts for
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stat.ML2022★ 110 cited
Maximizing information from chemical engineering data sets: Applications to machine learning
Alexander Thebelt, Johannes Wiebe, Jan Kronqvist +2
It is well-documented how artificial intelligence can have (and already is having) a big impact on chemical engineering. But classical machine learning approaches may be weak for m…
stat.ML2020★ 51 cited
ENTMOOT: A Framework for Optimization over Ensemble Tree Models
Alexander Thebelt, Jan Kronqvist, Miten Mistry +3
Gradient boosted trees and other regression tree models perform well in a wide range of real-world, industrial applications. These tree models (i) offer insight into important pred…