Optimal experimental design: Formulations and computations
Questions of 'how best to acquire data'are essential to modelling and prediction in the
natural and social sciences, engineering applications, and beyond. Optimal experimental …
natural and social sciences, engineering applications, and beyond. Optimal experimental …
No change, no gain: empowering graph neural networks with expected model change maximization for active learning
Abstract Graph Neural Networks (GNNs) are crucial for machine learning applications with
graph-structured data, but their success depends on sufficient labeled data. We present a …
graph-structured data, but their success depends on sufficient labeled data. We present a …
Regression tree-based active learning
Abstract Machine learning algorithms often require large training sets to perform well, but
labeling such large amounts of data is not always feasible, as in many applications …
labeling such large amounts of data is not always feasible, as in many applications …
Self-correcting bayesian optimization through bayesian active learning
Gaussian processes are the model of choice in Bayesian optimization and active learning.
Yet, they are highly dependent on cleverly chosen hyperparameters to reach their full …
Yet, they are highly dependent on cleverly chosen hyperparameters to reach their full …
Active learning for data streams: a survey
D Cacciarelli, M Kulahci - Machine Learning, 2024 - Springer
Online active learning is a paradigm in machine learning that aims to select the most
informative data points to label from a data stream. The problem of minimizing the cost …
informative data points to label from a data stream. The problem of minimizing the cost …
SOBER: Highly parallel Bayesian optimization and Bayesian quadrature over discrete and mixed spaces
Batch Bayesian optimisation and Bayesian quadrature have been shown to be sample-
efficient methods of performing optimisation and quadrature where expensive-to-evaluate …
efficient methods of performing optimisation and quadrature where expensive-to-evaluate …
[HTML][HTML] Active machine learning for chemical engineers: a bright future lies ahead!
By combining machine learning with the design of experiments, thereby achieving so-called
active machine learning, more efficient and cheaper research can be conducted. Machine …
active machine learning, more efficient and cheaper research can be conducted. Machine …
Weighted ensembles for adaptive active learning
Labeled data can be expensive to acquire in several application domains, including medical
imaging, robotics, computer vision and wireless networks to list a few. To efficiently train …
imaging, robotics, computer vision and wireless networks to list a few. To efficiently train …
Robust Gaussian Processes via Relevance Pursuit
S Ament, E Santorella, D Eriksson, B Letham… - arXiv preprint arXiv …, 2024 - arxiv.org
Gaussian processes (GPs) are non-parametric probabilistic regression models that are
popular due to their flexibility, data efficiency, and well-calibrated uncertainty estimates …
popular due to their flexibility, data efficiency, and well-calibrated uncertainty estimates …
Domain-agnostic batch Bayesian optimization with diverse constraints via Bayesian quadrature
Real-world optimisation problems often feature complex combinations of (1) diverse
constraints,(2) discrete and mixed spaces, and are (3) highly parallelisable.(4) There are …
constraints,(2) discrete and mixed spaces, and are (3) highly parallelisable.(4) There are …