26 citations · 31 across the 9 of their papers we have counts for
4 papers · 1 filter
Physics-Driven ML-Based Modelling for Correcting Inverse Estimation
Ruiyuan Kang, Tingting Mu, Panos Liatsis +1
When deploying machine learning estimators in science and engineering (SAE) domains, it is critical to avoid failed estimations that can have disastrous consequences, e.g., in aero…
Faster Riemannian Newton-type Optimization by Subsampling and Cubic Regularization
Yian Deng, Tingting Mu
This work is on constrained large-scale non-convex optimization where the constraint set implies a manifold structure. Solving such problems is important in a multitude of fundamen…
A Unified Theory of Diversity in Ensemble Learning
Danny Wood, Tingting Mu, Andrew Webb +3
We present a theory of ensemble diversity, explaining the nature of diversity for a wide range of supervised learning scenarios. This challenge has been referred to as the holy gra…
Data-driven Approaches to Surrogate Machine Learning Model Development
H. Rhys Jones, Tingting Mu, Andrei C. Popescu +1
We demonstrate the adaption of three established methods to the field of surrogate machine learning model development. These methods are data augmentation, custom loss functions an…