Search Results for author:
Found 25 papers, 13 papers with code
Transfer Learning Bayesian Optimization to Design Competitor DNA Molecules for Use in Diagnostic Assays
With the rise in engineered biomolecular devices, there is an increased need for tailor-made biological sequences.
Verifying message-passing neural networks via topology-based bounds tightening
Since graph neural networks (GNNs) are often vulnerable to attack, we need to know when we can trust them.
Mixed-Output Gaussian Process Latent Variable Models
Our framework allows the use of a range of priors for the weights of each observation.
Transition Constrained Bayesian Optimization via Markov Decision Processes
Bayesian optimization is a methodology to optimize black-box functions.
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.
Gaussian Processes for Monitoring Air-Quality in Kampala
Monitoring air pollution is of vital importance to the overall health of the population.
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.
Using Gaussian Processes to Design Dynamic Experiments for Black-Box Model Discrimination under Uncertainty
Typically, several rival mechanistic models can explain the available data, so design of dynamic experiments for model discrimination helps optimally collect additional data by finding experimental settings that maximise model prediction divergence.
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.
Design of Experiments for Verifying Biomolecular Networks
There is a growing trend in molecular and synthetic biology of using mechanistic (non machine learning) models to design biomolecular networks.
A disjunctive cut strengthening technique for convex MINLP
We prove that both types of cuts are valid and that the second type of cut can dominate both the first type and the original cut.
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.
Argumentation for Explainable Scheduling (Full Paper with Proofs)
Specifically, we define argumentative and natural language explanations for why a schedule is (not) feasible, (not) efficient or (not) satisfying fixed user decisions, based on models of the fundamental makespan scheduling problem in terms of abstract argumentation frameworks (AFs).
Data-driven optimization of processes with degrading equipment
In chemical and manufacturing processes, unit failures due to equipment degradation can lead to process downtime and significant costs.
Optimization and Control
GPdoemd: a Python package for design of experiments for model discrimination
Given rival mathematical models and an initial experimental data set, optimal design of experiments suggests maximally informative experimental observations that maximise a design criterion weighted by prediction uncertainty.
Mixed-Integer Convex Nonlinear Optimization with Gradient-Boosted Trees Embedded
Decision trees usefully represent sparse, high dimensional and noisy data.
Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches
Healthcare companies must submit pharmaceutical drugs or medical devices to regulatory bodies before marketing new technology.
Bayesian Optimization with Dimension Scheduling: Application to Biological Systems
But experiments may be less expensive than BO methods assume: In some simulation models, we may be able to conduct multiple thousands of experiments in a few hours, and the computational burden of BO is no longer negligible compared to experimentation time.
