Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces

1 code implementation • 2 Jul 2022 • Alexander Thebelt, Calvin Tsay, Robert M. Lee, Nathan Sudermann-Merx, David Walz, Behrang Shafei, Ruth Misener

Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning and neural architecture search, as they achieve good predictive performance with little or no manual tuning, naturally handle discrete feature spaces, and are relatively insensitive to outliers in the training data.

Bayesian Optimization Neural Architecture Search

Multi-Objective Constrained Optimization for Energy Applications via Tree Ensembles

1 code implementation • 4 Nov 2021 • Alexander Thebelt, Calvin Tsay, Robert M. Lee, Nathan Sudermann-Merx, David Walz, Tom Tranter, Ruth Misener

Energy systems optimization problems are complex due to strongly non-linear system behavior and multiple competing objectives, e. g. economic gain vs. environmental impact.

ENTMOOT: A Framework for Optimization over Ensemble Tree Models

1 code implementation • 10 Mar 2020 • Alexander Thebelt, Jan Kronqvist, Miten Mistry, Robert M. Lee, Nathan Sudermann-Merx, Ruth Misener

Gradient boosted trees and other regression tree models perform well in a wide range of real-world, industrial applications.

Decision Making