26 citations · 31 across the 9 of their papers we have counts for
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cs.LG2023★ 1 cited
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…
cs.LG2023
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…
cs.LG2023★ 26 cited
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…