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04/29/2023
When Deep Learning Meets Polyhedral Theory: A Survey
In the past decade, deep learning became the prevalent methodology for p...
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02/20/2023
Model-based feature selection for neural networks: A mixed-integer programming approach
In this work, we develop a novel input feature selection framework for R...
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02/03/2023
Distributional constrained reinforcement learning for supply chain optimization
This work studies reinforcement learning (RL) in the context of multi-pe...
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11/11/2022
Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization
Bayesian Optimization is a useful tool for experiment design. Unfortunat...
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07/02/2022
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 ...
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02/10/2022
P-split formulations: A class of intermediate formulations between big-M and convex hull for disjunctive constraints
We develop a class of mixed-integer formulations for disjunctive constra...
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02/04/2022
OMLT: Optimization Machine Learning Toolkit
The optimization and machine learning toolkit (OMLT) is an open-source s...
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01/31/2022
SnAKe: Bayesian Optimization with Pathwise Exploration
Bayesian Optimization is a very effective tool for optimizing expensive ...
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01/25/2022
Maximizing information from chemical engineering data sets: Applications to machine learning
It is well-documented how artificial intelligence can have (and already ...
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11/04/2021
Multi-Objective Constrained Optimization for Energy Applications via Tree Ensembles
Energy systems optimization problems are complex due to strongly non-lin...
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02/08/2021
Partition-based formulations for mixed-integer optimization of trained ReLU neural networks
This paper introduces a class of mixed-integer formulations for trained ...
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01/29/2021