110 citations · 304 across the 26 of their papers we have counts for
6 papers · 1 filter
Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces
Alexander Thebelt, Calvin Tsay, Robert M. Lee +4
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…
OMLT: Optimization & Machine Learning Toolkit
Francesco Ceccon, Jordan Jalving, Joshua Haddad +4
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 tra…
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…
Multi-Objective Constrained Optimization for Energy Applications via Tree Ensembles
Alexander Thebelt, Calvin Tsay, Robert M. Lee +4
Energy systems optimization problems are complex due to strongly non-linear system behavior and multiple competing objectives, e.g. economic gain vs. environmental impact. Moreover…
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…
Bayesian Optimization with Dimension Scheduling: Application to Biological Systems
Doniyor Ulmasov, Caroline Baroukh, Benoit Chachuat +2
Bayesian Optimization (BO) is a data-efficient method for global black-box optimization of an expensive-to-evaluate fitness function. BO typically assumes that computation cost of…