Search Results for author:
Found 16 papers, 7 papers with code
Transition Constrained Bayesian Optimization via Markov Decision Processes
Bayesian optimization is a methodology to optimize black-box functions.
Bayesian optimization as a flexible and efficient design framework for sustainable process systems
Bayesian optimization (BO) is a powerful technology for optimizing noisy expensive-to-evaluate black-box functions, with a broad range of real-world applications in science, engineering, economics, manufacturing, and beyond.
Mixed-Integer Optimisation of Graph Neural Networks for Computer-Aided Molecular Design
ReLU neural networks have been modelled as constraints in mixed integer linear programming (MILP), enabling surrogate-based optimisation in various domains and efficient solution of machine learning certification problems.
Practical Path-based Bayesian Optimization
There has been a surge in interest in data-driven experimental design with applications to chemical engineering and drug manufacturing.
When Deep Learning Meets Polyhedral Theory: A Survey
In the past decade, deep learning became the prevalent methodology for predictive modeling thanks to the remarkable accuracy of deep neural networks in tasks such as computer vision and natural language processing.
Model-based feature selection for neural networks: A mixed-integer programming approach
In this work, we develop a novel input feature selection framework for ReLU-based deep neural networks (DNNs), which builds upon a mixed-integer optimization approach.
Distributional constrained reinforcement learning for supply chain optimization
We introduce Distributional Constrained Policy Optimization (DCPO), a novel approach for reliable constraint satisfaction in RL.
Distributional Reinforcement Learning
Policy Gradient Methods
+2
Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization
Bayesian Optimization is a useful tool for experiment design.
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.
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 constraints intermediate to the big-M and convex hull formulations in terms of relaxation strength.
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.
SnAKe: Bayesian Optimization with Pathwise Exploration
Bayesian Optimization is a very effective tool for optimizing expensive black-box functions.
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.
Between steps: Intermediate relaxations between big-M and convex hull formulations
This work develops a class of relaxations in between the big-M and convex hull formulations of disjunctions, drawing advantages from both.
