Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures?

L Wimmer, Y Sale, P Hofman, B Bischl… - Uncertainty in …, 2023 - proceedings.mlr.press
The quantification of aleatoric and epistemic uncertainty in terms of conditional entropy and
mutual information, respectively, has recently become quite common in machine learning …

[HTML][HTML] A robust learning methodology for uncertainty-aware scientific machine learning models

EA Costa, CM Rebello, M Fontana, L Schnitman… - Mathematics, 2022 - mdpi.com
Robust learning is an important issue in Scientific Machine Learning (SciML). There are
several works in the literature addressing this topic. However, there is an increasing demand …

A probabilistic model for aircraft in climb using monotonic functional Gaussian process emulators

N Pepper, M Thomas, G De Ath… - … of the Royal …, 2023 - royalsocietypublishing.org
Ensuring vertical separation is a key means of maintaining safe separation between aircraft
in congested airspace. Aircraft trajectories are modelled in the presence of significant …

GAR: generalized autoregression for multi-fidelity fusion

Y Wang, Z **ng, WEI XING - Advances in Neural …, 2022 - proceedings.neurips.cc
In many scientific research and engineering applications where repeated simulations of
complex systems are conducted, a surrogate is commonly adopted to quickly estimate the …

Gar: Generalized autoregression for multi-fidelity fusion

Y Wang, Z **ng, WW **ng - arxiv preprint arxiv:2301.05729, 2023 - arxiv.org
In many scientific research and engineering applications where repeated simulations of
complex systems are conducted, a surrogate is commonly adopted to quickly estimate the …