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Found 6 papers, 5 papers with code
Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces
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.
OMLT: Optimization & Machine Learning Toolkit
The optimization and machine learning toolkit (OMLT) is an open-source software package incorporating neural network and gradient-boosted tree surrogate models, which have been trained using machine learning, into larger optimization problems.
Maximizing information from chemical engineering data sets: Applications to machine learning
For each of these four data characteristics, we discuss applications where these data characteristics arise and show how current chemical engineering research is extending the fields of data science and machine learning to incorporate these challenges.
Multi-Objective Constrained Optimization for Energy Applications via Tree Ensembles
Energy systems optimization problems are complex due to strongly non-linear system behavior and multiple competing objectives, e. g. economic gain vs. environmental impact.
Partition-based formulations for mixed-integer optimization of trained ReLU neural networks
This paper introduces a class of mixed-integer formulations for trained ReLU neural networks.
ENTMOOT: A Framework for Optimization over Ensemble Tree Models
Gradient boosted trees and other regression tree models perform well in a wide range of real-world, industrial applications.
