110 citations · 304 across the 26 of their papers we have counts for
29 papers
Optimizing over trained GNNs via symmetry breaking
Shiqiang Zhang, Juan S. Campos, Christian Feldmann +5
Optimization over trained machine learning models has applications including: verification, minimizing neural acquisition functions, and integrating a trained surrogate into a larg…
Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization
Jose Pablo Folch, Robert M Lee, Behrang Shafei +4
Bayesian Optimization is a useful tool for experiment design. Unfortunately, the classical, sequential setting of Bayesian Optimization does not translate well into laboratory expe…
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…
P-split formulations: A class of intermediate formulations between big-M and convex hull for disjunctive constraints
Jan Kronqvist, Ruth Misener, Calvin Tsay
We develop a class of mixed-integer formulations for disjunctive constraints intermediate to the big-M and convex hull formulations in terms of relaxation strength. The main idea i…
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…
SnAKe: Bayesian Optimization with Pathwise Exploration
Jose Pablo Folch, Shiqiang Zhang, Robert M Lee +5
Bayesian Optimization is a very effective tool for optimizing expensive black-box functions. Inspired by applications developing and characterizing reaction chemistry using droplet…